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Cartoonists have an excellent understanding of how stories are shaped in a concise way with an eye for design. Recently, cartoonist extraordinaire Roz Chast appeared in the New Yorker prompting DALL-E images and I was immediately drawn to her prompts above and beyond the actual output of the machine.
The article’s title, “DALL-E, Make Me Another Picasso, Please” is a play on words like the old Lenny Bruce joke about a genie in a bottle giving an old man anything he wants. The old man asks the genie to “make me a malted” and poof! the genie turns him into a milkshake.
Like the genie’s gift, AIs are powerful but unruly and open to abuse, making the intercession of a prompt engineer a new and important job in the field of data science. These are people who understand that in constructing a request they will rely on artful skill and persistence to pull a good (and non-harmful) result from the mysterious soul of a machine. The best AI prompt engineers would be those who would actually consider whether there is a need for more derivative Picasso art, or what obligations should be considered before asking a machine to plagiarize the work of a famous painter.
Lately, concerns have centered around whether DALL-E will change the already eternally muddy definition of artistic genius. But asking who gets to be called a creative misses the point. What is art, and who gets to claim the title of artist are philosophical (and infrequently ethical) questions that have been argued for millennia. They don’t address the fundamental fusion happening between data science and the humanities. Successful prompt craft, whether for DALL-E or GPT-3 or any future algorithm-driven image and language model, will come to require not only an engineer’s understanding of how machines learn, but an arcane knowledge of art history, literature and library science as well.
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Artists and designers who claim that this kind of AI will end their careers are certainly invested in how this integration will progress. Vox recently published a video titled “What AI art means for human artists” that explores their anxiety in a way that acknowledges there is a very real evolution at hand despite the current dearth of “prompt craft” and wordsmithing involved. People are just starting to realize that we may reach a point where trademarking a word or phrase would not protect intellectual property in the same way that it does currently. What aspect of a prompt could we even copyright? How would derivative works be acknowledged? Could there be a metadata tag on every image stating whether it is “appropriate or permitted for AI consumption?” No one seems to be mentioning these speed bumps in the rush to get a personal MidJourney account.
Alex Shoop, an engineer at DataRobot and an expert in AI systems design, shared a few thoughts on this. “I think an important aspect of the ‘engineer’ part of ‘prompt engineer’ will include following best practices like robust testing, reproducible results and using technologies that are safe and secure,” he said. “For example, I can imagine a prompt engineer would set up many different prompt texts that are slightly varied, such as ‘cat holding red balloon in a backyard’ vs. ‘cat holding blue balloon in backyard’ in order to see how small changes would lead to different results even though DALL-E and generative AI models are unable to create deterministic or even reproducible results.” Despite this inability to create predictable artistic outcomes, Shoop says he feels that at least testing and tracking the experimentation setups should be one skill he would expect to see in a true “prompt engineer” job description.
Before the rise of high-end graphics and user interfaces, most science and engineering students saw little need to study visual art and product design. They weren’t as utilitarian as code. Now technology has created a symbiosis between these disciplines. The writer who contributed the original reference text descriptions, the cataloguer who constructed the metadata for the images as they were scraped and then dumped into a repository, the philosopher who evaluated the bias implicit in the dataset all provide necessary perspectives in this brave new world of image generation.
What results is a prompt engineer with a combination of similar skill sets who understands the repercussions if OpenAI uses more male artists than female. Or if one country’s art is represented more than another’s. Ask a librarian about the complexities of cataloging and categorization as it has been done for centuries and they will tell you: it’s painstaking. Prompt engineering will require attention to relationships, subgroups and location, along with an ability to examine censorship and respect copyright laws. While DALL-E was being trained on representative images of the Mona Lisa, the humans in the loop with an awareness of these minutiae were critical to reducing bias and encouraging fairness in all outcomes.
It’s not just offensive abuses that can be easily imagined. In a fascinating turn of events, there are even multi-million-dollar art forgeries being reported by artists who use AI as their medium of choice. All enormous datasets or large networks of models contain, buried deep within the data, intrinsic biases, labeling gaps and outright fraud that challenge quick ethical solutions. OpenAI’s Natalie Summers, who runs OpenAI’s Instagram account and is the “human in the loop” responsible for enforcing the rules that are supposed to guard against output that could damage reputations or incite outrage, expresses similar concerns.
This leads me to conclude that to be a prompt engineer is to be someone not only responsible for creating art, but willing to serve as a gatekeeper to prevent misuse like forgeries, hate speech, copyright violations, pornography, deepfakes and the like. Sure it’s nice to churn out dozens of odd, slightly disturbing surreal Dada art ‘products,’ but there should be something more compelling buried under the mound of dross that results from a toss-away visual experiment.
I believe DALL-E has brought us to an inflection point in AI art, where both artists and engineers will need to comprehend how data science manipulates and enables behavior while also being able to understand how machine learning models work. In order to design the output of these machine learning tools, we will need experience beyond engineering and design, in the same way that understanding the physics of light and aperture takes photographic art beyond the mundane.
This diagram is an abbreviation of Professor Neri Oxman’s “Cycle of Creativity.” Her work with the Mediated Matter research group at the MIT Media Lab explored the intersection of design, biology, computing and materials engineering with an eye on how all these fields optimally interact with one another. Likewise, in order to become a “prompt engineer” (an as-yet nonexistent job title that has yet to be formally embraced by any discipline), you will need an awareness of these intersections that are as broad as hers. It’s a serious job with multiple specialties.
Future DALL-E artists, whether self-taught or schooled, will always need the ability to communicate and design an original point of view. Like any librarian with image metadata and curation skills; like any engineer able to structure and test reproducible results; like historians able to connect Picasso’s influences with what was happening in the world as he was painting about war and beauty, “prompt engineer” will be an artistic career of the future, requiring a blend of scientific and artistic talents that will guide the algorithm. It will continue to be humans who inject their ideas into machines in service of the newer and ever-changing language of creation.
Tori Orr is a member of DataRobot’s AI Ethics Communications team.
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As large amounts of data, from both external and internal data sources, have become central to running an organization, a pipeline of technical staffing roles has been developed to manage the collection and processing of that data.
Down in the engine room, if you will, is a data engineer who integrates multiple sources of data and manages the operations that make and keep the data available for business analysis.
On the top deck is the data analyst, who serves the data from largely pre-formed models to nontechnical business users so they can perform their work.
Mid-deck, between these two, is the data analytics engineer. This is a specialist who understands both data engineering technology and the data analysis needs of a business, and thus can build the analytical models that the upper-deck data analysts and business end users need to fulfill their roles.
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Therefore, a data analytics engineer is a person who combines the skills of the data analyst and software engineer to source and transform data for easy analysis. Because of their technical dexterity and business acumen, they have become quite valuable as members of the data team. This article details the duties and requisite skills of the analytics engineer, as well as the remuneration prospects of the role.
The analytics engineer is a member of a data team who is responsible for efficient, integrated data models and products. They build useful, well-tested and documented dataset representations and tools that the rest of the company can use to answer their questions.
They move and transform data from the source so that it can be easily analyzed, visualized and worked upon by the data analyst or business user. Not only that, but they have the technical skills to apply software engineering best practices such as Version Control and CI/CD, but also need to communicate effectively with stakeholders about the use of these tools.
The datasets created by a data analytics engineer allow end-users to comprehend and examine the information within the data. An analytics engineer combines business strategy and technical data knowledge to translate complex information and illustrate them clearly as visual representations known as data models. They collaborate with data analysts and data engineers to provide simple visual representations of data patterns and communicate their meaning to coworkers, stakeholders and end-users.
The transition to cloud data warehouses, evolution of self-service business intelligence (BI) tools and introduction of data ingestion tools have contributed to significant shifts in data tooling. Roles and responsibilities within traditional data teams are changing.
With the shift to an extract, load, transform (ELT) procedure, data now drops in the warehouse before it has been transformed. This creates an opportunity for skilled technical analysts who are both well-versed with the business and the technical skills required to model the raw data into neat, well-defined datasets. This requires the skills of both a software engineer and a data analyst, which the analytics engineer possesses.
Analytics engineers handle the data itself, as well as managing and sorting data. It is their job to make sure data is ingested, transformed, scheduled and ready to be used for analytics by all who may require it. Many analytics engineers are the orchestrators of the modern data stack, and they decide on and apply tools for ETL/ELT.
The analytics engineer is responsible for implementing and managing a data warehouse to ingest data. They also decide on the best tools to ingest data from different sources into this warehouse. Then they model the data to be used by analysts and schedule tests to simplify these models. The basic duties of the analytics engineer include:
Engineers are responsible for ingesting data into the warehouse and making sure that datasets are maintained. They are the first to be notified of any issue in the pipeline, so they can fix it.
This is the process of building visual representations of data and relating connections between different information locations and systems. Analytics engineers are charged with modeling raw data into datasets that enable analytics across the company. These datasets act as a central source of truth, making it easier for business analysts and other stakeholders to view and understand data in a database.
The engineer creates data pipelines and workflows to move data from one point to another, and coordinates the combining, verifying and storing of that data for analysis. The engineer understands everything about data orchestration and automation.
They enable other team members like data analysts and data scientists to be more effective. Whether by sharing tips for writing better SQL, reworking a dataset to contain a new metric or dimension, or training them on how to apply best practices for software engineering. This approach is called dataops (a methodology that integrates data engineering, data analytics and devops). A few best practices that can be optimized include version control, data unit testing as well as continuous integration and continuous delivery (CI/CD).
As a member of a team, they collaborate with team members to collect business requirements, define successful analytics outcomes and design data models.
Depending on the company and role specifications, a data analytic engineer may be required to perform some or all of the following:
The analytics engineer collects information, designs data models, writes code, maintains data documentation, collaborates with data team members and communicates results to concerned stakeholders. Therefore, the Analytics Engineer blends business acumen with technical expertise and alternates between business strategy and data development.
Every company or employer looks out for a specific set of skills that they require in an analytics engineer, but some general skills and competencies are vital for every analytics engineer. These skills are discussed subsequently.
Analytic engineers typically use SQL to write transformations within data models. SQL is one of the most important skills that you need to master to become an analytics engineer, since the major portion of the analytics engineer’s duties is creating logic for data transformations, writing queries and building data models.
SQL is closely related to Dbt in the language it utilizes, so knowledge of the former is required for the latter. Dbt is the leading data transformation tool in the industry, which is why it is most likely that the majority of analytics engineers use this to write their data models.
Knowledge of advanced languages like R and Python is crucial for analytics engineers to handle various data orchestration tasks. Many data pipeline tools utilize Python, and knowing how to code in it is extremely useful for writing your own pipeline as an engineer.
An analytics engineer needs to be conversant with the most popular tools in a modern data stack. This means possessing experience with ingestion, transformation, warehousing and deployment tools: if not comprehensive knowledge of them, then at least the basic concepts behind each of them. Learning one tool in each part of the stack may facilitate inferential understanding of the others.
An engineer needs to have experience with tools for building data pipelines. Some of these tools include data warehouses like Snowflake, Amazon Redshift and Google BigQuery; ETL tools like AWS Glue, Talend, or others — as well as business intelligence tools like Tableau, Looker, etc.
Communication is key for analytics engineers because it is their responsibility to ensure that everyone is updated on the status of data. They need to communicate with relevant individuals when data quality is compromised or when a pipeline is damaged, to understand what the business needs. They also need to collaborate with business teams and data analysts to understand what the business needs. If this isn’t done, erroneous assumptions can be made on defective data, and valuable ideas and opportunities will go unnoticed. It is imperative for an analytics engineer to develop and sustain multi-functional interactions with various teams across the business.
In sum, an analytics engineer must have a robust combination of technical dexterity and stakeholder management skills to succeed.
Analytics engineers in all industries and environments now have great prospects with good remuneration scales. According to Glassdoor, the average base salary is $91,188 and $111,038 in total annually in the U.S.
The analytics engineer is tasked with modeling data to provide neat and accurate datasets so that different users within and outside the company can understand and utilize them. The role involves gathering, transforming, testing and documenting data. It requires key skills in terms of communication, software engineering and programming.
The role of the analytics engineer is fairly new to the data analytics niche, but it is fast gaining traction and recognition as more and more people realize its worth.
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Longtime Prince collaborator David “Z” Rivkin recalled his early studio sessions with the musician, prior to his first record deal.
Rivkin’s first time in the studio with Prince was with the musician’s high school band, Grand Central.
“It was Prince and Andre Cymone and Morris Day. A trio,” Rivkin recalled to Sunset Sound Recorders (video below). “I didn’t think it was anything special.”
Asked if Prince’s “amazing talent” had been obvious, he replied: “Not at that time,” noting that everyone involved were “all amateur back then.”
Things were different after the band split. In 1976, as Prince began developing his solo career, manager Owen Husney booked him into Minneapolis’ Sound 80 Studio to track a new demo, and Rivkin was once again present.
“I don’t know what happened,” he said when asked if Prince had wanted him specifically. “But I was probably one of the only engineers that knew the street and knew what was going on, and I wasn’t doing television commercials like everybody else; that’s all they did.”
By that point the 18-year-old Prince was working by himself. “He had all these new songs that were great, and he had recorded every part on this little hand cassette machine. And he hummed the piano part, then he hummed the drum beat, then he hummed the guitar part,” Rivkin said. “We’d go around the room, and before he started the drums he’d listen to the drum part; same thing with the piano, same thing with the bass. He had planned it out and he was able to execute it all himself, which is really rare.”
Of particular note was Prince’s ability to be “objective” over his own playing, Rivkin added. “He didn't sound like it was one guy. He managed to put different personalities in different instruments… He got so comfortable with recording that he did a lot of it himself eventually.”
The demo led to the deal with Warner Brothers, and Rivkin accompanied Prince to the label’s Amigo Studios in North Hollywood, where “all the famous producers came into the room to see if Prince could actually do it himself.” While the subtext was to make a decision over which producer was to work with Prince, he famously stuck it out until they let him do it himself.
While the partnership with Rivkin continued right into the Paisley Park era, the engineer noted he’d never worked FOR Prince, but instead worked WITH him – meaning he was exempt from the artist’s notoriously demanding behavior. “He tortured a lot of people,” Rivkin pointed out. “He could be very hard on people… he’d focus on one person that he didn’t think was doing the job and he’d let ‘em have it.”
Describing him as “a very tough boss,” the engineer added: “I didn’t come under his wrath at all so I’m luckily [only] a witness to that. … He liked to keep people under his thumb. … He wasn’t just venting – it was a control thing. ‘Don’t tell ‘em things – let ‘em guess.’ He used to treat his band that way.”
Watch David “Z” Rivkin’s Interview
They took "going solo" to a whole new level.
The amount of energy used for computing is climbing at an exponential rate. Business intelligence and consulting firm Enerdata reports that information, communication and technology accounts for 5% to 9% of total electricity consumption worldwide.
If growth continues unabated, computing could demand up to 20% of the world's power generation by 2030. With power grids already under strain from weather-related events and the economy transitioning from fossil fuel to renewables, engineers desperately need to flatten computing's energy demand curve.
Members of Jon Ihlefeld's multifunctional thin film group are doing their part. They are investigating a material system that will allow the semiconductor industry to co-locate computation and memory on a single chip.
"Right now we have a computer chip that does its computing activities with a little bit of memory on it," said Ihlefeld, associate professor of materials science and engineering and electrical and computer engineering at the University of Virginia School of Engineering and Applied Science.
Every time the computer chip wants to talk to memory the larger memory bank, it sends a signal down the line, and that requires energy. The longer the distance, the more energy it takes. Today the distance can be quite far -- up to several centimeters.
"In a perfect world, we would get them in direct contact with each other," Ihlefeld said.
That requires memory materials that are compatible with the rest of the integrated circuit. One class of materials suitable for memory devices are ferroelectrics, meaning they can hold and release a charge on demand. However, most ferroelectrics are incompatible with silicon and do not perform well when made very small, a necessity for modern-day and future miniaturized devices.
Researchers in Ihlefeld's lab are playing matchmaker. Their research advances materials with electrical and optical properties that make modern computation and communication possible, a research strength of the Department of Materials Science and Engineering. They also specialize in fabrication and characterization of a range of materials, a research strength of the Charles L. Brown Department of Electrical and Computer Engineering.
Their material of interest is hafnium oxide, which is used in the manufacture of cell phones and computers today. The downside is that in its natural state, hafnium oxide is not ferroelectric.
A Tip of the Cap to Shelby Fields
Over the last 11 years, it has become known that hafnium oxide's atoms can be manipulated to produce and hold a ferroelectric phase, or structure. When a hafnium oxide thin film is heated, a process called annealing, its atoms can move into the crystallographic pattern of a ferroelectric material; when the thin film is cooled, its crystalline structure sets in place.
Why formation of the ferroelectric phase happens has been the subject of much speculation. Shelby Fields, who earned a Ph.D. in materials science engineering from UVA this year, published a landmark study to explain how and why hafnium oxide forms into its useful, ferroelectric phase.
Fields' paper, Origin of Ferroelectric Phase Stabilization via the Clamping Effect in Ferroelectric Hafnium Zirconium Oxide Thin Films, published in August in Advanced Electronic Materials, illustrates how to stabilize a hafnium oxide-based thin film when it is sandwiched between a metal substrate and an electrode. Previous research found that more of the film stabilizes in the ferroelectric crystalline phase when the top electrode is in place for thermal annealing and cooling.
"The community had all sorts of explanations for why this is, and it turns out we were wrong," Fields said. "We thought the top electrode exerted some kind of mechanical stress, radiating laterally across the plane of the electrode, that prevented the hafnium oxide from stretching out and returning to its natural, non-ferroelectric state. My research shows that the mechanical stress moves out of plane; the electrode has a clamping effect."
The whole sandwich -- the substrate, thin film and electrode -- is a capacitor, and this finding could very well alter the materials that semiconductor manufacturers select as electrodes.
"Now we understand why the top layer is such an important consideration. Down the line, people who want to integrate computing and memory on a single chip will have to think about all the processing steps more carefully," Fields said.
Fields' paper summarizes the concluding chapter of his dissertation research. In prior published research, Fields demonstrated techniques to measure very thin films and mechanical stresses; the miniscule materials made stress measurements experimentally difficult.
Contributors in this collaborative research include group members Samantha Jaszewski, Ale Salanova and Takanori Mimura as well as Wesley Cai and Brian Sheldon from Brown University, David Henry from Sandia National Labs, Kyle Kelley from Oak Ridge National Lab, and Helge Heinrich from UVA's Nanoscale Materials Characterization Facility. Funding awarded through the U.S. Department of Energy's 3D Ferroelectric Microelectronics Energy Frontier Research Center and the Semiconductor Research Corporation supported the research.
"We wanted to go beyond anecdotal descriptions and provide data to back up our characterization of the material's behavior," Fields said. "I am glad we could provide the community with greater clarity regarding this clamping effect. We know the top layer matters a lot and we can engineer that top layer to Boost the clamping effect, and perhaps engineer the bottom layer to help with this effect, too. The ability to leverage a single experimental variable to control the crystalline phase would be a huge advantage for the semiconductor field. I would love for someone to ask and answer that question."
O Marks the Spot
That someone could be Samantha Jaszewski, a Ph.D. student of materials science and engineering and a member of Ihlefeld's Multifunctional Thin Film research group. Jaszewski also wants to understand what contributes to the stability of hafnium oxide's ferroelectric phase and how chip designers can control the material's behavior.
Jaszewski's research focuses on the atomic make-up of hafnium oxide in its natural and ferroelectric phase, with specific attention on the role of oxygen atoms. Her landmark study, Impact of Oxygen Content on Phase Constitution and Ferroelectric Behavior of Hafnium Oxide Thin Films Deposited by Reactive High-Power Impulse Magnetron Sputtering, is published in the October 2022 issue of Acta Materialia.
Hafnium oxide, as the name suggests, is composed of hafnium and oxygen atoms. "Sometimes we are missing those oxygen atoms in certain places, and that helps stabilize the ferroelectric phase," Jaszewski said.
The natural, non-ferroelectric state can tolerate a number of these oxygen vacancies, but not as many as needed to stabilize the ferroelectric phase. The precise concentration and location of oxygen vacancies that makes hafnium oxide ferroelectric has proven elusive because there aren't many tools available to make a definitive measurement.
Jaszewski worked around that problem by using several different techniques to measure oxygen vacancies in the team's thin films and correlated that with ferroelectric properties. She discovered that the ferroelectric phase requires a much higher number of oxygen vacancies than previously thought.
X-ray photoelectron spectroscopy was the go-to tool to calculate oxygen vacancy concentrations. Jaszewski discovered that there are contributing factors beyond what users of this spectroscopy technique typically measure, leading to a vast undercount of the oxygen vacancies.
Jaszewski's experiments also reveal that oxygen vacancies may be one of, if not the, most important parameters to stabilize the ferroelectric phase of the material. More research needs to be done to understand how the vacancies exist. She would also like to have other research teams measure the oxygen vacancies using her method to validate her findings.
Jaszewski's research overturns conventional wisdom, which suggested that the size of the crystal -- called a grain -- is what stabilizes the hafnium oxide. Jaszewski made three samples with equal grain sizes and different oxygen vacancy concentrations. Her research shows that the phases present in these samples varied, leading to the conclusion that oxygen vacancy concentration is more important than grain size.
Jaszewski first-authored the paper, which was co-authored by group members Fields and Salanova with collaborators in many research groups within and outside of UVA. Jaszewski's research is funded by her National Science Foundation graduate research fellowship and the Semiconductor Research Corporation.
Jaszewski is deepening her inquiry into hafnium oxides to explain the material's response to the application of an electric field. In the semiconductor industry, this phenomenon is referred to as wake-up and fatigue.
"When you apply an electric field to this material, the ferroelectric properties increase, or 'wake-up.' As you continue to apply the electric field, the ferroelectric properties degrade, in a process known as fatigue," Jaszewski said.
She has found that when an electric field is initially applied, it boosts the ferroelectric structure, but there are diminishing returns.
"As you continue to apply the field the ferroelectric properties degrade," Jaszewski said.
The next step is investigating how the oxygen atoms' choreography in the material contributes to wake-up and fatigue, which requires study of where vacancies are located dynamically.
"These landmark studies explain why ferroelectric hafnium oxide exists and how it stabilizes," Ihlefeld said. "Based on these new findings, we can engineer hafnium oxide thin films to be even more stable and perform even better in an actual application. By doing this fundamental research we can help semiconductor firms understand the origin of problems and how to prevent them in future production lines."
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Editor’s note: Not all players will have accompanying analysis after their pick.
Without further ado, the first pick in the 2022-2023 fantasy basketball draft goes to …
No surprise here. Jokic is a fantasy basketball cheat code after posting a historic 27.1 points, 13.8 rebounds, 7.9 assists, 1.3 threes and 2.4 stocks (steals plus blocks) per game with 58/34/81 shooting splits last season. He had the highest single-season Box Plus/Minus (BPM) in NBA history and is the unanimous first-overall pick here.
Coming off his best statistical season and playing a career-high 68 games, Embiid will be the focal point of a fantasy-friendly Sixers offense. As one of the most dominant big men in the league, he finished fourth in the NBA in double-doubles with 2.7 stocks on the defensive end. He should have no issues replicating a top-three performance on a per-game and totals basis if he can play at least 60 games this year.
Durant finished second on a per-game basis in 2021-22, and he’ll continue to see a 30 percent usage rate this season. I expect the Nets to finish top-10 in Pace and Offensive Rating, and KD will lead the way. He checks every box for fantasy basketball.
Giannis is the only player in NBA history to average at least 25 points, 10.0 rebounds, 5.0 assists, 1.0 steal and 1.0 block in multiple seasons, and he’s done it four years in a row. He finished 10th on a per-game basis last season, but an uptick in three-pointers made while shooting over 72% from the free-throw line will help justify drafting him here.
Luka Magic is in effect. Yeah, he’s known to turn the ball over at a high rate and miss a ton of free throws, but I’m buying his numbers after the Kristaps Porzingis trade. 31.3 points, 9.5 rebounds, 8.1 assists with 3.9 threes and 1.0 steal. He’s ripe for an MVP-like season.
I moved Tatum up in my latest mock draft because I think he’ll be more valuable outside of scoring and threes than Stephen Curry. He’ll command a higher usage rate, shoots a higher percentage from the field, and can check every box, similar to Kevin Durant.
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Original take: Still only 24 years old, Tatum put up career-highs in points, rebounds and assists last year with a 32% usage rate. DAWG.
The 2022 Finals MVP and four-time champion annually anchors the points, threes and free throw percentage categories in fantasy basketball. I project Curry’s minutes and usage will remain in the 30s as the centerpiece of one of the best teams in basketball.
Haliburton moves up because Harden looks passive on offense. While he’s still a first-round pick, I’m moving Harden lower (as you’ll see below) as he’s becoming more of a facilitator at this stage in his career.
Original take: If you don’t know, now you know. I’ve seen Haliburton selected in the top five in industry expert drafts, but I’m comfortable grabbing him in the top 10. I’m teetering between Haliburton and LaMelo Ball in this spot, but the former is a good bet to average the most assists per game this year and score over 20 points per night with solid peripherals. In 18 games without Malcolm Brogdon last season, Haliburton tallied 18.6 points, 10.1 assists, 4.2 rebounds, 2.3 threes and 1.8 steals with 50/46/89 shooting splits. Breakout!
Lillard was 13th on my draft board, but he gets a substantial boost after Harden falls, LaMelo Ball‘s ankle injury and Karl-Anthony Towns’ recovery from an illness. Lillard is healthy and has a better team, so fantasy managers should feel comfortable selecting Lillard as a top-10 pick.
Towns gets a slight boost here from pick 11 as he’s back on the court and played well in his only preseason appearance. Now, Rudy Gobert was in street clothes, but Towns looks back to form despite the reported weight loss due to his illness — nineteen points with six rebounds and six assists in his preseason debut.
Original take: The Timberwolves’ frontcourt suddenly got crowded after they traded for Utah Jazz All-Star center and three-time Defensive Player of the Year Rudy Gobert this offseason. Gobert’s presence should slide KAT to a true-stretch four, which has risks and benefits. He finished eighth on a per-game basis last year, but I’d expect his rebound numbers to drop slightly with a boost in made threes. KAT is accustomed to playing with an offensively limited frontcourt (Taj Gibson, Jarred Vanderbilt), so I’m not overly concerned about Gobert being in town. He should still be able to collect at least two stocks and be the best-shooting big man in the league. Forty-one percent from beyond the arc last year and 40% for this career, just sayin’.
My previous take had Harden “squarely in the top-10” conversation, but from what I’ve seen in the preseason, Harden is closer to last year’s version than the Harden of old. Still, he’s a first-round player for his cross-categorical contributions.
Original take: Harden’s decision to make less and return to Philly on a two-year deal shows he’s ready to ball. The hamstring injury that’s plagued him for two seasons is behind him, and he’s reportedly in great shape heading into this season. He was the only player in the NBA to post over 20+ points and 10+ assists per game in ’21-’22 and finished 15th on a per-game basis despite changing teams midseason and playing on a bum leg. A bounce-back should put him squarely in the top 10 in fantasy basketball.
A sprained ankle will cost the dynamic point guard the first couple of weeks of the regular season. He’s moving down a few spots merely because of the injury. He should still be in line for another standout, All-Star-level season.
Original take: LaMelo Ball enters his third NBA season looking to build off career-highs in points, rebounds, assists, threes made, and FT%. He’s also among the league leaders in steals. The loss of Miles Bridges might decrease his passing numbers after accounting for 23% of Ball’s assists in ’21-’22, but there’s still plenty of opportunity to see a 30% usage while filling up the box score alongside Terry Rozier.
The Suns seem like they’re going through it, but I don’t think it’ll have much bearing on Devin Booker’s production this season. He’s in his prime and an improved playmaker who’s also a walking bucket.
He’ll play more than 29 games this year, and the Nets new “big 3” looked great in Wednesday’s matchup versus the Bucks. Uncle Drew will get his, scoring-wise, but there’s still upside in him as a distributor and rebounder at the position. His high efficiency and sneaky steals make him an early second-round pick in my book.
My suspicions are trending in the right direction, as Trae Young’s assists were not what we’ve come to expect in accurate years. Granted, it’s the preseason, but I think Dejounte Murray will continue to eat into his assists despite being one of the best scorers in the league.
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Original take: Trae Young has a new backcourt mate in All-Star guard Dejounte Murray, but he should still be one of the best fantasy guards this upcoming season. He’s a volume scorer who ranked fourth in usage rate last year at 34.4. I expect Hawks head coach Nate McMillan to stagger Young and Murray’s minutes at the point but also provide Young the chance to work more off-ball to create better looks on the perimeter. He should still boast substantial fantasy numbers without much offense around him and Murray.
While this may seem high for a player coming off of injury, Kawhi is one of the best two-way players in fantasy while sporting elite shooting percentages. Paul George admitted that Kawhi is the alpha for the Clippers, and despite being load managed at times, he’s one of the best on a per-game basis in fantasy basketball.
He might be better than Trae Young in fantasy this season. I’m here for another monster season, live from the A.
Will he play center or not? Not sure that it matters much for fantasy considering he just needs to STAY HEALTHY. Davis was a top-12 player on a per-game basis last season before getting injured so going back to the well. He’s too talented to go beyond the first 18 picks.
The run on perennial All-Stars continues. George is the second option of arguably the best roster in the Western Conference. He might take games off here and there, but PG, similar to Kawhi Leonard, is an exceptional two-way player. If he can cut down on his turnovers and Boost his shooting from the field, he could return to first-round value this year.
The King is back and ready to make yet another run for a title. It helps that his teammates are healthy, and while his scoring will likely take a dip, he’s one of a handful of players capable of a triple-double every night.
You’ll likely have to pay up for Ant-man, but he is undoubtedly in breakout territory. He’s well-positioned to make the jump into an All-Star this year. He’s a great source of points, steals, threes and his athleticism makes him a solid rebounder and blocker at his position.
Drafting a player from the Sacramento Kings this early may feel odd, but Sabonis is one of fantasy basketball’s most versatile big men. He won’t do a ton defensively, but he’ll be a reliable source of points, rebounds, assists and FG% as a second-round pick.
Minimal competition in the backcourt, plus he’ll likely be top five in minutes played. He’s a perfect selection when punting field-goal percentage (pair with Luka Doncic, perhaps?) but also will be among the league leaders in threes, steals and FT percentage.
His assists are down since Kyle Lowry arrived, but he’s attempting more threes in the preseason than he has in his career. He is a nice grab here for his defensive stats and double-double potential.
Mitchell finished in the top 30 in per-game value last season and top 50 the year prior. The Cavs are deep, but he’ll be the primary scorer on one of the most fantasy-friendly teams in the league. I’m expecting his points to dip, but he’ll still be one of the better guards of fantasy.
One of my breakouts, so grabbing him as an early third-round pick is the way to go.
Siakam is a stat stuffer who will finish in the top five in minutes played. He’s an ideal fit for any fantasy managers interested in punting blocks, rebounds, and FG%.
He’s got the sauce, plus he’s an exceptional passer and facilitator who can score at will. He and Donovan Mitchell can thrive together and should make for one heck of a dynamic duo in fantasy. I’m still expecting him to be one of the top distributors but provide points, threes, steals and an elite FT percentage in category leagues.
Zion tweaked his ankle in his last preseason game, but Pelicans HC Willie Green and Zion both said that “he’s doing fine.” I’m confident he is still worthy of being selected in the first three rounds.
Shape-memory metals, which can revert from one shape to a different one simply by being warmed or otherwise triggered, have been useful in a variety of applications, as actuators that can control the movement of various devices. Now, the discovery of a new category of shape-memory materials made of ceramic rather than of metal could open up a new range of applications, especially for high-temperature settings, such as actuators inside a jet engine or a deep borehole.
The new findings will be reported in the journal Nature, in a paper by former doctoral student Edward Pang PhD '21 and professors Gregory Olson and Christopher Schuh, all in MIT's Department of Materials Science and Engineering.
Shape-memory materials, Schuh explains, have two distinct shapes, and can switch back and forth between them. They can be easily triggered by temperature, mechanical stress, or electric or magnetic fields, to change shape in a way that exerts force, he says.
"They are interesting materials because they're sort of like a solid-state piston," he says -- in other words, a device that can push against something. But while a piston is an assembly of many parts, a "shape-memory material is a solid-state material that does all of that. It doesn't need a system. It doesn't need many parts. It's just a material, and it changes its shape spontaneously. It can do work. So, it's interesting as a 'smart material,'" he says.
Shape-memory metals have long been used as simple actuators in a variety of devices but are limited by the achievable service temperatures of the metals used, usually a few hundred degrees Celsius at most. Ceramics can withstand much higher temperatures, sometimes up to thousands of degrees, but are known for their brittleness. Now, the MIT team has found a way to overcome that and produce a ceramic material that can actuate without accumulating damage, thus making it possible for it to function reliably as a shape-memory material through many cycles of use.
"The shape-memory materials that are out there in the world, they're all metal," says Schuh. "When you change a material's shape down at the atomic level, there's a whole lot of damage that can be created. Atoms have to reshuffle and change their structure. And as atoms are moving and reshuffling, it's sort of easy to get them in the wrong spots and create defects and damage the material, which leads them to fatigue and eventually fall apart."
He adds that "you end up with materials that can deform a few times, but then eventually they degrade and they can fall apart. And because metals are so ductile, they're a little more damage resistant, and so the field has really focused on metals because when a metal is damaged on the inside, it can tolerate it."
Ceramics, by contrast, don't tolerate damage well at all, and normally don't bend but fracture. Zirconia is one that is known to have a shape-memory property, but it accumulates damage very easily during a shape memory cycle -- a property measured as high hysteresis. "What we wanted to do with this work was design a new ceramic and specifically target that hysteresis. We wanted to design a ceramic where the [shape] transformation is somehow still gigantic: We want to do a lot of work. But internally, at the atomic scale, it's more gentle."
Schuh explains that Pang, who led the work, "took all of the modern tools of science, everything you can name -- computational thermodynamics, phase transformation physics, crystallographic calculations, machine learning -- and he put all these tools together in a totally new way" in order to solve the problem of creating such a material.
The result was a new variation of zirconia. "Basically, it's zirconia," Schuh says. "It looks and smells and tastes just like zirconia that people already know and use, including like cubic zirconia in jewelry." But some atoms of different elements have been introduced into its structure in a way that alters some of its properties. These elements "dissolve into the lattice, and they sculpt it, and they change that transformation, they make it more gentle at the atomic scale."
The hysteresis changed so dramatically that it now resembles that of metals, Schuh says. "That was a huge, huge change -- we're talking about a factor of 10." And the deformation that the material can achieve amounts to about 10 percent, meaning that a rod of this material could get 10 percent longer when triggered -- enough to do significant work.
One common application of shape-memory materials is relief valves, where if a tank of something exceeds a certain critical temperature, the valve is triggered by that heat, automatically opening to relieve pressure and prevent explosion. The new ceramic material could now extend that capability to far higher-temperature situations than present materials could handle.
For example, actuators that direct airflow inside a jet engine might be a useful application, Pang says. While the overall environment there is hot, there are various channels of airflow being controlled, so those flows could be used to trigger a shape-memory ceramic by directing cooler or hotter air on the device as needed.
Today, shape-memory ceramics that exist "are sort of a laboratory curiosity," because they fall apart after a few cycles, Schuh says. "This is a step in the direction of making something that can reproducibly and reliably operate many, many times in service."
The team plans to continue exploring the material, finding ways to produce it in bigger batches and more complex shapes, and testing its ability to withstand many cycles of transformation.
What attracted him to this project in the first place, Schuh says, is its potential for broad applications. "There are things we do with complex mechanical systems that have lots of parts and assemblies, and the idea that you can replace a complicated package of things with a single material that has the functionality built in at the atomic scale -- to me, that's attractive because it makes large, complicated things into small, simple things. In some ways it's like replacing vacuum tubes with transistors."
While it's hard to predict the areas where this material will find its first practical uses, Schuh says that, for example, "it's very hard to scale down a hydraulic piston. It's hard to make that on the micro scale." But now, "the idea that you have a solid-state version of that at very small scales -- I've always felt there are a lot of applications for microscale motions. Microrobots in small places, lab-on-a-chip valves, lots of small things that need actuation could benefit from smart materials like this."
The work was supported by the U.S. Army Research Office, in part through MIT's Institute for Soldier Nanotechnologies, and by the U.S. National Science Foundation.
Electrical engineers have been designing custom circuit boards on computers for years, but this approach simply moved the paper and pencil method to digital. It didn’t advance the craft or make it easier for engineers to reuse design elements across designs. They always have had to start from scratch, even if they can apply learnings they gathered along the way.
Jitx, a startup from three Berkeley alumni, recognized that the digital approach to board design hadn’t changed much and they saw an opening. They have created a new way of designing boards using code to describe what the board looks like. Today the company announced the general availability of that product and a previously unannounced $12 million Series A.
“Today engineers use very graphical tools to manually design schematics and circuit boards, and that’s just not productive enough anymore. There’s too much design work. The systems are too complex to design them without errors. So Jitx is changing that by letting engineers write code to create their designs, and that brings the benefits of software to hardware design,” co-founder and CEO Duncan Haldane told TechCrunch.
He says that this has several advantages, including reusability, increased productivity, a simpler and automated review process and the ability to share and collaborate on designs in meaningful ways.
The company has come up with a coding language that Haldane says is aimed squarely at electrical engineers and how they work.
“We made a programming language and a compiler for electrical engineers. So basically an electrical engineer thinks about problems a certain way — I need to design this IoT device. I need to have this radio. I want it to last five years with this type of battery. And I need this microcontroller — and we just gave them a language to express that so you can start at a really high level, and then the compiler starts filling in details for you,” he said.
He says the language is something engineers should understand, given they already write Python scripts to test board designs now.
The other important thing that Jitx does is make it simpler to test the design to make sure it’s correct. Today that involves eyeballing and getting a bunch of people together to critique it to make sure it’s going to work. Jitx wants to bring automation to the design review process to test the design automatically, and move it into production more quickly.
The roots of the idea go back to work that Haldane was doing as a grad student in 2013. As the idea of code-based circuit design grew, he connected with his co-founders and started the company in 2017.
The startup began as a design consultancy helping companies design circuit boards, but they discovered this way of bringing more automation to the design process and began building the solution they have today, originally for themselves, before making it into a product.
The company currently has 18 employees. He says growing a diverse workforce is challenging, especially in a field that requires highly specialized skills, but the company is working at it and looking at ways to find candidates from historically underrepresented groups. “The way we address inclusion is just you have to work really hard on sourcing people. You’re not going to hire someone because they’re diverse, but rather because they’re less represented, so you have to work harder to find them,” he said.
The $12 million investment closed last year. It was led by Sequoia Capital with participation from Y Combinator, Funders Club and Liquid 2. The company was a member of the Y Combinator Summer 2018 cohort and was one of the 10 startups that caught the eye of the TechCrunch reporters covering their Demo Day.
Despite an impressive pandemic boom, Twilio, like many other tech giants, got hit hard by tech's accurate economic downturn.
Just last month, the cloud-communications platform laid off 11% of its staff, cutting members of the workforce in its research-and-development and administrative departments. But according to its careers page, Twilio is still hiring in key areas, such as engineering, software development, and product, with roles paying upward of $120,000.
While Twilio does not publicly share how much it pays employees, it's required to disclose salary offers in work-visa applications submitted to the US Office of Foreign Labor Certification. Insider combed through the data from the office, which authorizes the hiring non-US citizens via H-1B visas and publicly releases the data.
The data does not include stock grants, which can significantly increase total compensation, but it's a valuable guide to Twilio salaries for a variety of positions. Insider dug through the hundreds of disclosed salaries that represent engineers, managers, and more.
The analyzed salaries come from the second quarter of 2022 and mainly represent workers in California, Washington, and Colorado. The broad listed categories are taken from the government filings. Roles are listed below only if there are at least two candidates.
When you are done checking out the data below, take a look at Insider's Big Tech salary database to see how much Apple, Microsoft, Google, Facebook, and other companies pay their workers.
This weed whacks you.
An has inventor boggled minds online after apparently outfitting a plant with software that enables it to swing a machete like some sort of chlorophyll-consuming cyborg.
A video detailing the so-called “bushwhacker” currently boasts over 10.9 million views on Twitter as gawkers wonder about the usefulness of a weapon-wielding weed.
According to inventor David Bowen, the “plant machete” was created by outfitting a live philodendron — a genus of flowering plant — with software that processes its electrical signals and translates them into movement.
“The system uses an open source micro-controller connected to the plant to read varying resistance signals across the plant’s leaves,” Bowen writes on his site regarding the “Poison Ivy”-evoking invention. “Using custom software, these signals are mapped in real-time to the movements of the joints of the industrial robot holding a machete.”
The artist added, “In this way, the movements of the machete are determined based on input from the plant.”
Bowen said the micocontroller is essentially the brain of the robot “controlling the machete determining how it swings, jabs, slices and interacts in space.” Think the way Alex Murphy’s human-bot brain allows him to control his synthetic body in “Robocop.”
Accompanying footage shows the slice-n-dicing shrubbery twirling the machete with the deftness and fluidity of a samurai.
It’s yet unclear what the plant machete’s purpose would be, other than perhaps being used by eco-terrorists to provide illegal loggers ironic comeuppance.
Bowen hasn’t yet replied to the Post’s request for comment.
However, the dystopian bush-bot certainly made an impression on Twitter with one social media personality, Rex Chapman, writing: “Why????????”
“While you studied photosynthesis I studied the blade,” quipped one Twitter wit, while another joked: “watched out vegans.”
“I can’t wait to get home, approach my wife, show the video, explain why there was a tweet of a plant swinging a machete, and then show her this tweet,” exclaimed one viewer. “It’s going to be hilarious if I can get it done.”
This isn’t the first time inventors have freaked out the internet with a bizarre cybernetic hybrid. This summer, Texas scientists developed veritable spider-bots by robotically manipulating dead arachnids so they can grasp objects with their legs.
Software Developers, Applications
AR/VR Software Development Engineer: $153,000 to $200,000
Engineering Project Manager: $135,000 to $278,750
Machine Learning Engineer: $137,000 to $174,256
Production Services Engineer: $159,375 to $196,500
Software Development Engineer: $120,000 to $291,725
Software Development Engineer - Applications: $90,750 to $246,000
Software Development Engineer - Firmware: $125,000 to $208,120
Software Development Engineer - Systems: $115,000 to $236,900
Software Development Engineer - Test: $138,000 to $220,000
Software Development Engineer - UI: $132,000 to $205,000
Software Integrity Engineer: $112,961 to $174,000
Tools & Automation Engineer: $122,120 to $200,000
Software Developers, Systems Software
AR/VR Software Development Engineer: $127,000 to $230,000
AR/VR Software Engineer: $144,900 to $174,000
Configuration/Release Engineer: $125,000 to $180,200
Engineering Project Manager: $137,500 to $209,873
Machine Learning Engineer: $120,000 to $260,500
Machine Learning Research Engineer: $147,660 to $225,000
Production Services Engineer: $140,000 to $203,480
Software Developer, Systems Software: $90,750 to $184,080
Software Development Engineer: $130,000 to $263,000
Software Development Engineer - Applications: $143,040 to $247,000
Software Development Engineer - Data: $150,000 to $250,000
Software Development Engineer - Firmware: $109,920 to $245,000
Software Development Engineer - Security: $155,000 to $263,000
Software Development Engineer - Server: $176,000 to $191,686
Software Development Engineer - Systems: $111,980 to $247,881
Software Development Engineer - Test: $132,000 to $208,000
Software Development Engineer - UI: $170,000 to $195,535
Software Integrity Engineer: $135,000 to $190,000
Systems Design Engineer: $130,000 to $172,794
Test Engineer: $142,358 to $206,000
Tools & Automation Engineer: $124,000 to $180,000
Financial Analyst: $94,224 to $158,464
Business Intelligence Analysts
Business Systems Analyst: $157,850 to $165,238
Data Scientist: $143,200 to $187,600
Financial Analyst: $137,824 to $155,333
Operations Research Analysts
Business Systems Analyst: $108,938 to $157,850
Data Scientist: $119,000 to $210,000
Engineering Project Manager: $135,000 to $205,000
Machine Learning Engineer: $155,000 to $205,000
Operations Research Analyst: $84,822 to $184,850
Professional Services Consultant: $148,000 to $205,000
Project Manager: $125,000 to $188,000
Strategic Sourcing Manager: $110,000 to $223,850
Supply Demand Planner: $123,000 to $155,000
WW SDM Planner: $135,000 to $149,831
Market Research Analysts and Marketing Specialists
Alliance/Partner Specialist: $140,000 to $194,757
Engineering Project Manager: $145,000 to $181,889
Global Supply Manager: $145,000 to $185,000
Management Analyst: $93,558 to $136,573
Professional Services Consultant: $143,500 to $180,000
Strategic Sourcing Manager: $125,000 to $172,771
Computer and Information Research Scientists
Data Scientist: $120,000 to $205,000
Machine Learning Engineer: $170,893 to $231,750
Machine Learning Research Engineer: $141,050 to $247,006
Network and Computer Systems Administrators
Production Services Engineer: $120,000 to $152,955
Computer Hardware Engineers
Design Verification Engineer: $122,040 to $217,835
Hardware Development Engineer: $163,000 to $233,000
Software Development Engineer: $126,459 to $208,875
Computer Systems Analysts
Data Engineer: $170,000 to $173,097
Engineering Project Manager: $161,600 to $172,422
Computer and Information Systems Managers
Data Scientist Manager: $180,595 to $263,731
Engineering Project/Program Manager: $176,930 to $227,944
Information Security Manager: $215,000 to $241,606
Machine Learning Manager: $169,048 to $300,000
Production Services Manager: $168,569 to $204,000
Software Development Engineer - Applications Manager: $173,102 to $290,000
Software Development Engineer - Manager: $174,449 to $270,000
Software Development Engineer - Systems Manager: $200,100 to $240,000
Software Development Engineer - Test Manager: $170,000 to $295,000
Software Development Engineer Manager: $171,000 to $282,500
Software QE Engineer Manager: $161,670 to $217,203
Analog IC Design Engineer: $165,000 to $195,000
CPU Implementation Engineer: $155,000 to $190,000
Design Verification Engineer: $108,124 to $200,000
Electrical Engineer: $92,040 to $174,845
Engineering Program Manager: $180,000 to $190,000
Engineering Project Manager: $140,000 to $195,000
FE Engineer: $123,557 to $185,000
Hardware Development Engineer: $115,000 to $220,000
Hardware Systems Engineer: $130,000 to $215,000
Implementation Engineer: $125,235 to $167,468
Power Engineer: $145,676 to $179,566
Product Compliance Engineer: $156,494 to $167,363
RF/Analog/Mixed Signal Engineer: $144,172 to $156,398
RFIC Design Engineer: $140,000 to $195,000
SoC Physical Design Engineer, PnR: $135,000 to $190,000
Software Development Engineer - Firmware: $115,000 to $160,000
System Design Engineer: $175,000 to $195,000
Systems Design Engineer: $103,880 to $220,000
Wireless Systems Engineer: $185,000 to $213,876
Electronics Engineers, Except Computer
Acoustics Engineer: $149,864 to $165,000
ASIC Design Engineer: $105,080 to $222,248
Design Verification Engineer: $121,626 to $244,485
Electronic Design Engineer: $140,000 to $171,500
Electronics Engineer, Except Computer: $110,822 to $172,245
Engineering Project Manager: $147,679 to $192,021
FE Engineer: $108,124 to $241,842
Formal Validation Engineer: $150,000 to $205,664
GPU Static Timing Analysis Engineer: $135,000 to $165,000
Hardware Development Engineer: $135,000 to $285,000
Hardware Systems Engineer: $134,350 to $213,151
Implementation Engineer: $120,000 to $210,000
Layout Engineer: $134,138 to $194,750
Machine Learning Engineer: $142,500 to $189,000
Performance and Modeling Engineer: $138,372 to $174,299
Power Engineer: $139,150 to $194,780
RF Systems Engineer: $155,000 to $200,000
RF Test Engineer: $151,039 to $155,250
RF/Analog/Mixed Signal Engineer: $144,172 to $215,000
Silicon Prototype Engineer: $135,000 to $170,000
Silicon Validation Engineer: $131,250 to $191,298
SoC Physical Design Engineer, PnR: $145,000 to $195,000
SoC Physical Design Engineer, Top Level: $150,000 to $185,000
Software Development Engineer: $104,000 to $224,452
Software Development Engineer - Firmware: $112,254 to $212,357
Software Development Engineer - Systems: $146,000 to $195,800
Software Development Engineer - Test: $174,156 to $206,676
Software Integrity Engineer: $128,000 to $217,500
Systems Design Engineer: $137,000 to $212,580
Tools & Automation Engineer: $110,000 to $142,800
VLSI CAD Engineer: $140,806 to $214,702
Wireless Systems Engineer: $139,029 to $200,000
Wireless Systems Integration and Test Development Engineer: $165,000 to $195,000
Advanced Manufacturing Engineer: $135,000 to $170,000
Engineering Project Manager: $130,000 to $191,005
Global Supply Manager: $145,000 to $185,000
Industrial Engineer: $103,189 to $144,248
Manufacturing Design Engineer: $124,200 to $203,335
Manufacturing Quality Engineer: $123,228 to $180,000
Operations Engineer Program Manager: $140,000 to $178,537
Operations Engineering Program Manager: $162,469 to $191,187
Operations Program Manager: $145,000 to $165,000
Product Quality Engineer: $135,000 to $190,000
Product Quality Manager: $140,000
Project/Program Manager: $151,598
Strategic Sourcing Manager: $105,000 to $155,000
Supply Chain Program Manager: $131,764 to $146,353
Technical Program Manager: $125,000 to $153,000
Human Factors Engineers and Ergonomists
Human Factors Design Engineer: $128,750 to $236,900
Hardware Development Engineer: $157,000 to $176,000
Manufacturing Design Engineer: $124,044 to $165,000
Materials Engineer: $94,536 to $190,000
Product Design Engineer: $168,488 to $207,679
Reliability Engineer: $137,400 to $180,000
Advanced Manufacturing Engineer: $125,000 to $150,833
Engineering Project Manager: $135,000 to $160,000
Hardware Development Engineer: $125,000 to $190,000
Machine Learning Research Engineer: $149,833 to $193,463
Manufacturing Design Engineer: $125,757 to $176,280
Manufacturing Quality Engineer: $139,310 to $150,000
Mechanical Design Engineer: $142,500 to $170,000
Mechanical Engineer: $82,368 to $146,744
Product Design Engineer: $120,000 to $220,000
Product Quality Engineer: $133,487 to $180,000
Reliability Engineer: $150,000 to $165,000
Architectural and Engineering Managers
Design Verification Engineer Manager: $201,844 to $248,692
Engineering Project/Program Manager: $192,192 to $222,029
Hardware Development Manager: $200,750 to $300,000
Systems Design Manager: $206,147 to $270,500
Hardware Development Engineer: $140,000 to $170,000
Software Quality Assurance Engineers and Testers
Software Development Engineer: $120,000 to $198,000
Software Development Engineer - Applications: $154,000 to $175,000
Software Development Engineer - Test: $129,413 to $196,000
Software Integrity Engineer: $99,445 to $179,000
Software Quality Assurance Engineer and Tester: $70,346 to $168,834
Tools & Automation Engineer: $110,000 to $210,877
Multimedia Artists and Animators
HI Designer: $139,000 to $220,000
Commercial and Industrial Designers
HI Designer: $196,000 to $230,000
Graphic Designer: $62,733 to $92,851
HI Designer: $140,000 to $275,000
Marcom Designer: $137,000 to $165,000
Industrial Production Managers
Manufacturing Design Engineer Manager: $161,599 to $177,654
Manufacturing Quality Engineer Manager: $165,867 to $210,137.
Operations Engineering Management: $187,476 to $208,030
Strategic Sourcing Manager: $146,925 to $194,204
Technical Program Manager: $140,000 to $185,000
Information Technology Project Managers
Engineering Project Manager: $130,000 to $250,000
Marketing Manager: $169,790 to $275,000
Software Development Engineer - Applications: $205,373 to $207,000
Legal Counsel: $210,000 to $225,000
Engineering Program Specialist: $110,000 to $125,000
Engineering Project Manager: $152,201 to $187,963
Logistics Analyst: $130,640 to $135,000
Strategic sourcing manager: $130,000 to $178,500
Technical Program Manager: $144,333 to $169,260
WW SDM Planner: $116,210 to $172,757
Data Engineer: $143,416 to $175,000
Data Scientist: $130,000 to $209,000
Machine Learning Engineer: $128,268 to $204,750
Accountants and Auditors
Financial Analyst: $110,000 to $181,406
Are you a tech employee with insights to share on Big Tech compensation? Got a tip? Contact Diamond Naga Siu securely at email@example.com or firstname.lastname@example.org, at 310-986-1383 on Signal and Telegram, or @diamondnagasiu on Twitter.