Engineering · Salary Guide
Machine Learning Engineer Salary Guide (2026)
ML engineer compensation in the US is one of the most variable in tech — entry levels start around $115k and LLM research engineers at top AI labs clear $1M total comp. This guide breaks down the numbers by experience, city, and company — and the research vs. applied-ML split that decides where you land.
US national median (base salary)
$195k
Total compensation typically runs 30–60% above base at public tech companies.
Salary by experience level
| Experience level | Base salary (US national) |
|---|---|
| Entry (0–2 yrs) | $115k – $170k |
| Mid (3–5 yrs) | $160k – $235k |
| Senior (6–9 yrs) | $215k – $340k |
| Staff / Principal (10+ yrs) | $310k – $600k |
Salary by city
Base salary ranges adjusted by local market multipliers. Click through for the full city guide with tax notes and cost-of-living context.
| City | Adjusted range | Market note | |
|---|---|---|---|
| San Francisco | $216k – $459k | Highest base salaries in the US but COL roughly 2x national average. | Full guide |
| New York | $205k – $435k | Finance premium for quant/fintech roles. Strong Big Tech satellite offices. | Full guide |
| Seattle | $192k – $408k | No state income tax. Dominated by Amazon and Microsoft; AWS roles pay a premium. | Full guide |
| Austin | $168k – $357k | Lower base salaries but strong take-home due to zero state tax and ~40% lower housing costs than SF. | Full guide |
| Los Angeles | $184k – $391k | Strong media/entertainment tech market. Base salaries below SF but above most US markets. | Full guide |
| Remote (US) | $152k – $323k | Remote-first companies typically pay at 85–105% of SF rates regardless of employee location. | Full guide |
Top employers and their ranges
PPUs replace RSUs; research-engineer roles top the market
Research engineer ladder pays above product SWE
GenAI org has standing ~15% uplift over general SWE
Applied ML roles (pricing, search) sit just below research labs
Strong applied-ML ladder; Michelangelo platform heritage
Single-tier senior band; cash-heavy like other Netflix roles
Skills that boost salary
Negotiation tactics
- 1Separate research-engineer from applied-ML offers in your mind. Research-engineer offers at frontier labs (OpenAI, Anthropic, DeepMind) clear $500k–$1M+ but have tiny hiring bars; applied ML at large companies is easier to get but caps around $450k TC.
- 2Get competing offers from at least one frontier lab if at all possible. Even a non-accepted offer from OpenAI or Anthropic moves any other ML offer by 20–30%.
- 3Negotiate compute access, not just salary. At senior levels, a firm commitment of X GPU-hours/quarter for research time is worth more than a $20k base bump for your next career move.
- 4Publish. First-author NeurIPS/ICML/ICLR papers add 15–30% to offers at labs and Big Tech research orgs. One good paper can outperform two years of promotion velocity.
- 5Benchmark against MLE, not SWE, ladders. ML roles at most Big Tech companies sit on a separate, higher band than general SWE — confirm before accepting a 'leveled-in' offer that assumes parity.
FAQ
What is the average ML engineer salary in the US?
As of 2026, the median base salary for a US ML engineer is about $195,000, with mid-level ranges of $160k–$235k. Total compensation varies widely — applied ML engineers at Big Tech clear $300k–$450k TC, while research engineers at frontier AI labs reach $700k–$1.1M+.
How much do research engineers at OpenAI or Anthropic make?
Research engineers at OpenAI and Anthropic typically earn $350k–$700k total comp at the mid level and $700k–$1.1M+ at the senior level. Compensation is paid in base plus PPUs (OpenAI) or equity (Anthropic). Offers above $1M generally require a published research track record.
Do ML engineers get paid more than software engineers?
Yes. ML engineers earn roughly 15–25% more than same-level SWEs at most Big Tech companies in 2026, and the gap widens at research labs. Inside a single company, MLE and SWE may sit on separate ladders with different compensation bands.
Is it worth specializing in LLMs for salary growth?
In 2026, yes. LLM fine-tuning, RLHF, and distributed-training skills command the highest premiums in the market — 20–45% above general ML roles. The tradeoff is that the field moves fast; engineers who don't stay current on post-training methods see premiums fade within 12–18 months.
Related Machine Learning Engineer Resources
Sources: US Bureau of Labor Statistics — Occupational Employment and Wages, May 2025 · Levels.fyi aggregated offer data (Q1 2026) · H1B Salary Database (2025–2026 filings) · Built In salary reports (2026)
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