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Home / Compensation / Anthropic / ML Engineer
Total compensation for a Anthropic ML Engineer ranges from about $305K at the entry level to $2.5M+ at the most senior bands, with a typical mid-level package around $680K. 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) |
|---|---|---|---|---|---|
| L3MTS, Research / ML (Junior) | 1-3 YOE | $250K | $95K | - | $305K - $480K |
| L4MTS, Research / ML | 3-7 YOE | $290K | $210K | - | $410K - $770K |
| L5Senior MTS, Research / ML | 6-10 YOE | $330K | $340K | - | $540K - $1.1M |
| L6Staff MTS, Research / ML | 10-15 YOE | $370K | $595K | - | $770K - $1.6M |
| L7Senior Staff MTS, Research / ML | 13+ YOE | $430K | $960K | - | $1.1M - $2.5M |
Junior ML IC. Most ML hires are PhD-track and start at L4+.
Mid-level ML IC. Most-common external research hire.
Senior ML IC. Premium for pretraining / alignment work.
Staff ML IC. Counter-offers from OpenAI / DeepMind set the top end.
Senior staff. Realized value depends on Anthropic growth.
Anthropic does not formally distinguish ML engineers from MTS - 'Member of Technical Staff' is the umbrella IC title. ML/research-track ICs negotiate at the top end of the SWE band; pre-training and alignment work commands premium offers, with counter-offers from OpenAI / Google DeepMind setting the upper bound.
6 rounds. 5 of them can be practised here - each one below links the rep that trains it.
Recruiter screen
30 minutes. Background, level calibration, team interest (research engineering vs product engineering vs infra). Introduction to the take-home project.
Take-home project
4-8 hours. A realistic engineering task - often involves building a small system, extending an existing codebase, or writing a technical analysis. Submitted before the onsite and reviewed by your interviewers beforehand.
Free rep: Coding challenges with a real sandbox →Take-home review (technical)
60 minutes. Deep dive on your submitted project. Interviewers have read your code and will probe decisions: why this approach, what are the failure modes, how would you extend it. This is the round where shallow work is exposed.
Free rep: Coding challenges with a real sandbox →Onsite: Applied coding
60 minutes. Practical problems - API design, data pipeline logic, evaluation tooling, debugging. Less algorithmic puzzle, more 'here's a real problem we encounter, solve it.' Python is standard.
Free rep: Coding challenges with a real sandbox →Onsite: System design
60 minutes. ML-infrastructure flavored: model serving architecture, prompt management systems, evaluation pipelines, rate-limited inference APIs. Anthropic thinks in distributed systems AND ML systems simultaneously.
Free rep: Design walkthrough: Design a Distributed Cache (Memcached / Redis Cluster) →Onsite: Values and mission
45-60 minutes. Not a soft round. Anthropic screens for authentic engagement with AI safety and alignment. Expect questions about how you think about model behavior, capability vs safety tradeoffs, and your personal motivation for working on these problems.
Free rep: STAR answers: Dealing with Ambiguity →The full Anthropic prep pack → what they value, topic emphasis, and the questions candidates ask.
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