Highest Paying Tech Companies in 2026: Real Numbers by Level
The Bureau of Labor Statistics pegs the national median for software developers at $133,080. That number is accurate and almost useless. An L5 at Google earns $410K total comp. A senior engineer at Anthropic clears $563K. A quant developer at Citadel can pull $660K in a good year. The range is so wide that the median tells you almost nothing about what a specific company will actually pay.
This post breaks down real numbers from levels.fyi, which aggregates verified salary submissions, cross-checked against what we’ve observed in how candidates talk about comp expectations during mock interviews on LastRound AI. One thing that stands out: candidates dramatically underestimate how much the compensation structure, not just the total number, affects the real value of an offer. More on that below.
The AI Labs Are Now Paying More Than FAANG
This is the biggest shift since 2021’s ZIRP-era bidding wars. Three AI labs have blown past traditional Big Tech in median total comp.
OpenAI sits at $805K median total comp per year for software engineers, with L5 packages around $878K. That’s not a signing bonus story. That’s base ($325K at L5) plus equity, which has appreciated dramatically since the company’s valuation rounds. Anthropic is just behind at $710K median, with senior engineers starting at $563K and leads at $785K. Both numbers come from levels.fyi data updated June 2026.
xAI is in a different position. Median reported total comp is around $640K, but the sample size is smaller and the company is younger, so I’d treat those figures as approximate. The spread between submissions is wider than at OpenAI or Anthropic.
What’s driving these numbers is partly genuinely competitive hiring and partly equity appreciation. A lot of those “total comp” figures at OpenAI include equity valued at the most recent round price. Whether that equity is worth what level.fyi’s formula implies is a separate question, and an honestly uncertain one.
Netflix: Still the Cash King, With a Catch
Netflix’s model is different from every other company on this list. Their philosophy has always been to pay in cash, set a high salary ceiling, and skip most of the equity game. The levels.fyi data shows a range of $218K at the low end to $1.22M for the most senior ICs, with a median around $450K.
The catch is the band is genuinely wide, and Netflix uses it aggressively. They’ll set your salary at whatever they think is market rate for your specific skills at hire. If you don’t negotiate hard, you can leave 30-40% on the table relative to someone with similar experience who did. They also have no guaranteed bonus structure, which matters when you’re doing the math against an offer that includes a $50K annual bonus and RSU vesting.
Traditional FAANG: Google, Meta, Amazon
Google’s levels are still the benchmark most candidates anchor to. Per levels.fyi:
- L3 (entry): $212K total comp
- L4: $312K
- L5 (senior): $410K
- L6 (staff): $576K
Meta runs a bit hotter at the mid levels. E5 median is $468K versus Google L5 at $410K. Meta’s E6 jumps to $708K. The company tends to front-load stock grants and has been cutting headcount in waves since 2022, so the per-remaining-employee comp has gone up. Whether that dynamic persists is anyone’s guess.
Amazon is lower than people expect. Median across all levels is around $277K, and the structure is weird: Amazon caps base salary at $350K and loads the rest into RSUs with a back-weighted vesting schedule (5% year one, 15% year two, 40% year three, 40% year four). The effective comp in year one and two looks low on paper. Candidates who see an “$800K total comp” Amazon offer and compare it against a Netflix cash offer sometimes miscalculate by $200K or more.
The Quant Trading Firms Most Engineers Ignore
Jane Street, Citadel Securities, and Two Sigma don’t show up on most “highest paying tech companies” lists because they don’t market themselves as tech companies. They’re wrong to exclude them.
Citadel quant developers earn a median $660K, with the structure heavily weighted toward bonus rather than base or equity. Per levels.fyi, the average breakdown is roughly $316K base with $306K in bonus, and essentially no equity. Jane Street’s software engineers are at $350K median, but that data likely undercounts the actual range because Jane Street employees are notoriously reluctant to share numbers publicly.
These roles are harder to get and harder to leave. If you thrive at one and get a bad year bonus, the hit is real. But the upside is also real.
Cash vs. Equity: The Calculation Most Candidates Skip
Before comparing total comp across companies, figure out what percentage is equity and what the liquidity situation is. Public company RSUs vest and sell immediately. Private company equity (OpenAI, Anthropic, xAI) requires a liquidity event. Netflix pays in cash. Amazon’s vesting schedule back-loads your comp by two years. Same headline number, wildly different actual value.
What This Means for Interview Prep, Specifically
There’s a pattern we see on LastRound AI that’s worth naming: candidates preparing for high-comp roles at OpenAI, Anthropic, or Citadel show up expecting questions similar to a standard Google interview. They aren’t. AI lab interviews often go deeper on ML fundamentals, system design for training infrastructure, and probabilistic reasoning. Citadel and Jane Street interviews have a math and coding component that looks nothing like LeetCode.
The implication is that “I want to work at the highest-paying company” is a different prep plan depending on which company you mean. If you’re targeting the AI labs, the machine learning interview questions and system design preparation matter more than grinding 150 LeetCode problems. If you’re targeting Google L5, algorithmic coding is still the gate.
For anyone comparing FAANG-tier offers against each other, the FAANG salary comparison breakdown goes deeper on offer negotiation tactics and how to read the actual comp documents, not just the headline number.
One Thing I’d Argue About
The conventional advice is to optimize for learning early in your career and chase comp later. I’m not sure that’s right anymore. The gap between a company paying $180K and one paying $350K for the same role has compounded into six-figure wealth differences over a four-year window. That’s not a small rounding error. It’s a down payment on a house, or five years of financial optionality.
I think the real advice is: optimize for learning AND compensation at companies where those aren’t in conflict. A job at a top AI lab pays well AND will teach you things no other environment will. The framing of “pick learning or money” was always a bit of a false choice. It’s just that now the spread makes it impossible to ignore.
Whether the AI lab equity actually pays out is genuinely uncertain. I don’t know. Neither does anyone else until there’s a liquidity event.
