Trends in Compensation Packages for Tech Workers

AI isn’t just reshaping products. It’s reshaping pay too. We compiled data points on Levels.fyi from the past year for Software Engineers in the Bay Area, Seattle, and NYC and broke them down by the ML / AI specialization vs. all others. The results are pretty striking. Here’s how median total compensation compares, level by level: 🎓 Entry-Level Engineer • Non-AI: $174K • AI/ML: $188K 👨💻 Software Engineer (mid-level) • Non-AI: $242K • AI/ML: $290K 💻 Senior Engineer • Non-AI: $320K • AI/ML: $400K 🧠 Staff Engineer • Non-AI: $400K • AI/ML: $526K 🚀 Principal Engineer • Non-AI: $556K • AI/ML: $770K The higher you go in level, the wider the gap in pay between someone specialized in ML / AI vs someone who's not. AI roles aren’t just paying slightly more, they’re redefining what top-of-band looks like for individual contributors. Especially at the Staff and Principal levels, where we’re seeing comp packages define its own category, and rival senior director / VP level comps. This shift isn’t just about job titles, it’s about the strategic weight companies are putting behind AI right now. And perhaps a story about how companies with heavy capital expenditure (ie training models) are re-writing the salary caps for individual ICs and employees since it's no longer the largest expense. And we’ll likely see these premiums ripple through compensation bands for the rest of the industry in 2025. We’re tracking these changes every day in our benchmark tool, DM me if you'd like to get access. Would you switch teams or companies to work on AI if it meant an extra $100K+? View AI SWE salaries here: https://lnkd.in/dxbAQrnq #salarytransparency

Carta's H1 2024 data confirms companies have become leaner and salary benchmarks have remained flat across much of their ecosystem. Here are my key takeaways from Carta's latest data release: 🗒️ Salary and equity stability: Salaries and equity for new hires have stayed unchanged since September 2023. This suggests a new normal for compensation packages, following significant drops in previous years. 🗒️ Hiring trends: Hiring activity has not picked up. January through April 2024 saw fewer new hires than any corresponding months in the past four years. Total net headcount remained flat. 🗒️ Decline in Layoffs: Although layoffs are still occurring, the number of monthly job departures has steadily declined this year, which feels like progress. 🗒️ High turnover in recent hires: Nearly a quarter of employees hired in 2022 left their company before their first work anniversary, which is super costly to companies. 🗒️ Geo-located compensation: Smaller startups are more likely to adjust pay based on location. Nearly 90% of startups valued under $25 million do this, compared to 71% of companies valued at $1 billion or more. Also, 41 of 54 major US markets saw startup pay move closer to the top market rates, with big jumps in midwest cities like Columbus and Cincinnati. Leaner teams and flat pay packages show that companies have become far more intentional with their finances given this economy. As Peter Walker says, "Bragging rights have very quickly moved from "total employees" to "revenue per employee". It will be fascinating to see if startups can hold onto these more fiscally responsible practices if and when larger financing rounds return. Note: Don't miss the addendum that includes updated benchmark equity values for advisors, early employees, and startup board members. The full report is linked in the comments below 👇

SWE base salaries are getting higher and higher and the companies paying those higher base salaries aren’t necessarily the ones paying the most in total compensation. How come? 🤔 Here are two tables that show the top paying companies for SWEs based on base salary and then based on total compensation, for data submitted to Levels.fyi between May ‘24 and May ‘25. These submissions are from Tier 1 locations (Bay Area, Seattle, New York, and LA) to normalize for location-based pay, and are for data points submitted with less than 10 years of experience. Although there are some similarities between the two charts, what’s most interesting to me are the *differences*. When comparing with only base salaries, we see that (other than Netflix with their flexible comp structure) it is only Anthropic and OpenAI that have medians of >$300k for base. The story here is that, in the AI race, these companies likely have to use really high cash compensation to make up for their lack of liquid equity comp (although OpenAI still handsomely pays in equity too). On the base salary side, we continue to see more top-tier scaleups (like StubHub) and hedge funds/quant firms (like Citadel and Jane Street) as opposed to any big tech names. It is only on the total comp table that we start to see more Big Tech names like Airbnb, Pinterest, Snap Inc., and Meta (Facebook). These submissions were also filtering for non-equity growth data points, meaning these numbers are for new offers only, and they’d be what the employee would see on their offer letter. The competition for top-tier talent continues to rage on, especially in the AI world. As companies are looking for new ways to compensate for the best of the best, we’ve been seeing trends in base pay growing to new heights, private companies providing more liquidity events, and some incredibly high paper money stock grants. What are your thoughts on these changes, and did you notice anything else interesting about these two tables? View AI SWE salaries on Levels.fyi here: https://lnkd.in/gdd6JTCx