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Home / Compensation / Nvidia / ML Engineer
Total compensation for a Nvidia ML Engineer ranges from about $180K at the entry level to $2.5M+ at the most senior bands, with a typical mid-level package around $530K. 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) |
|---|---|---|---|---|---|
| IC3ML Engineer (New Grad) | 0-2 YOE | $150K | $62K | $13K | $180K - $360K |
| IC4ML Engineer | 2-5 YOE | $185K | $135K | $25K | $270K - $640K |
| IC5Senior ML Engineer | 5-9 YOE | $240K | $245K | $40K | $395K - $985K |
| IC6Staff ML Engineer | 8-14 YOE | $295K | $445K | $65K | $605K - $1.6M |
| IC7Distinguished ML Engineer | 13+ YOE | $350K | $760K | $90K | $880K - $2.5M |
Entry-level. Equity is the dominant component due to NVDA appreciation.
Mid-level. CUDA / kernel work pulls top of band.
Senior IC. Top-of-band offers concentrated in ML systems.
Staff IC. Foundation-model training and inference systems premium.
Distinguished. One of the highest-paying ML IC bands in tech.
Nvidia ML / deep-learning engineer comp tracks SWE bands closely with a notable premium for CUDA, kernel, GPU-compiler, and large-scale-training systems work. NVDA stock appreciation has substantially boosted realized comp for tenured ML engineers.
6 rounds. 5 of them can be practised here - each one below links the rep that trains it.
Recruiter screen
30 minutes. Background, team (frameworks, inference, applied AI), and level.
Technical screen
60 minutes. Coding plus deep learning questions.
Free rep: Coding challenges with a real sandbox →Onsite: Coding
60 minutes. Python and/or C++, sometimes performance-flavored.
Free rep: Coding challenges with a real sandbox →Onsite: Deep learning depth
60 minutes. Architectures, training dynamics, and your past work.
Free rep: Machine learning questions →Onsite: Performance / systems
60 minutes. GPU memory, parallelism, and inference optimization.
Free rep: Design walkthrough: Design an LLM Inference / RAG Platform →Onsite: Hiring manager
45 minutes. Ownership, collaboration, and team fit.
Free rep: STAR answers: Intellectual Honesty (Nvidia Culture) →The full Nvidia prep pack → what they value, topic emphasis, and the questions candidates ask.
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