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Home / Compensation / OpenAI / ML Engineer
Total compensation for a OpenAI ML Engineer ranges from about $305K at the entry level to $2.8M+ at the most senior bands, with a typical mid-level package around $740K. Below is the full breakdown by level - base salary, annual equity, and bonus - from public-source data.
Public-source estimates (Beta). Last updated 2026-05. Figures are annualized USD.
| Level | Years | Base (p50) | Equity/yr (p50) | Bonus (p50) | Total (p25-p90) |
|---|---|---|---|---|---|
| L3ML Engineer (Junior) | 1-3 YOE | $245K | $95K | - | $305K - $525K |
| L4ML Engineer | 3-7 YOE | $285K | $215K | - | $425K - $830K |
| L5Senior ML Engineer | 6-11 YOE | $330K | $370K | - | $580K - $1.2M |
| L6Staff ML Engineer | 10-15 YOE | $380K | $605K | - | $820K - $1.8M |
| L7Senior Staff ML Engineer | 13+ YOE | $450K | $990K | - | $1.1M - $2.8M |
Junior ML IC. Most ML hires arrive at L4+.
Mid-level ML IC. Most-common external research hire.
Senior ML IC. Counter-offers from Anthropic / DeepMind set the band.
Staff ML IC. PPU cap is a meaningful constraint.
Senior staff. Foundation-model leads pull top of band.
OpenAI does not strongly distinguish ML / research engineers from SWE - the org is research-heavy throughout. Comp tracks SWE bands but skews 5-15% higher for pretraining and capabilities work, with frequent counter-offer escalation against Anthropic and DeepMind. PPU cap structure applies.
6 rounds. 5 of them can be practised here - each one below links the rep that trains it.
Recruiter screen
30 minutes. Background, team (research engineering, applied AI, infrastructure), and level.
Technical screen
60 minutes. Practical coding; some teams add ML questions.
Free rep: Coding challenges with a real sandbox →Onsite: Coding
60 minutes. Practical, often systems- or ML-flavored coding.
Free rep: Coding challenges with a real sandbox →Onsite: ML depth
60 minutes. Deep learning fundamentals and your past ML work.
Free rep: Machine learning questions →Onsite: ML infrastructure design
60 minutes. Training pipelines, evaluation harnesses, or inference serving.
Free rep: Design walkthrough: Design an LLM Inference / RAG Platform →Onsite: Behavioral / mission
45-60 minutes. Mission alignment, ambiguity, and high-stakes judgment.
Free rep: STAR answers: Mission Alignment and Safety Judgment (OpenAI Culture) →The full OpenAI prep pack → what they value, topic emphasis, and the questions candidates ask.
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The offer calculator compares your current and target offers 4 years out - vesting, refresh grants, and federal + state + FICA tax - so you negotiate with real take-home numbers.
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