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Home / Compensation / Microsoft / ML Engineer
Total compensation for a Microsoft ML Engineer ranges from about $150K at the entry level to $1.3M+ at the most senior bands, with a typical mid-level package around $355K. 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) |
|---|---|---|---|---|---|
| 59 (AS)Applied Scientist (New Grad) | 0-2 YOE | $130K | $20K | $15K | $150K - $215K |
| 60-61 (AS II)Applied Scientist II | 2-5 YOE | $170K | $45K | $22K | $200K - $340K |
| 62-63 (Sr AS)Senior Applied Scientist | 5-9 YOE | $215K | $100K | $38K | $290K - $530K |
| 64-65 (Principal AS)Principal Applied Scientist | 9-14 YOE | $260K | $215K | $55K | $425K - $840K |
| 66+ (Partner AS)Partner Applied Scientist | 13+ YOE | $315K | $340K | $95K | $600K - $1.3M |
Entry-level. PhD candidates often start at 60.
Mid-level. Common entry for fresh ML PhDs.
Senior IC. Azure AI / Copilot orgs hire heavily here.
Principal IC. Foundation-model premium begins.
Partner. External hires very rare.
Microsoft applied scientist / ML engineer roles share the same numeric levels as SDE. Azure AI, Bing, Copilot, and MSR (Research) are the major hiring areas. Comp tracks SDE within ~5% with a small premium at 64+ for foundation-model work.
7 rounds. 6 of them can be practised here - each one below links the rep that trains it.
Recruiter screen
30 minutes. Background, title (Applied Scientist vs ML engineer), and level.
Technical screen
45-60 minutes. Coding and/or ML fundamentals.
Free rep: Coding challenges with a real sandbox →Onsite: Coding
45-60 minutes. Algorithms with clean, working code.
Free rep: Coding challenges with a real sandbox →Onsite: ML fundamentals / applied ML
45-60 minutes. Models, evaluation, and applied problem framing.
Free rep: Machine learning questions →Onsite: Project deep dive
45-60 minutes. Your past ML work or research, with detailed follow-ups.
Free rep: What this round looks for →Onsite: ML system design (team-dependent)
45-60 minutes. Common for production ML teams.
Free rep: Design walkthrough: Design an ML Model Serving Platform (TorchServe / Triton) →Onsite: As appropriate (AA)
45-60 minutes. Senior interviewer; broad judgment and fit.
Free rep: STAR answers: Growth Mindset (Microsoft Value) →The full Microsoft prep pack → what they value, topic emphasis, and the questions candidates ask.
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