MLOps Engineer vs AI Safety Researcher: Which Pays More?
Side-by-side salary comparison by city, experience level, and career growth outlook. Data reflects current market rates.
MLOps Engineer
Build and maintain infrastructure for deploying machine learning models at scale.
AI Safety Researcher
Research methods to make AI systems safer, more aligned, and interpretable.
MLOps Engineer earns more on average — the national median is $17,640/year (22%) higher than a AI Safety Researcher. However, salaries vary significantly by city, employer, and experience level — see the city-by-city breakdown below.
MLOps Engineer vs AI Safety Researcher — Salary by City
National median figures in USD across top cities.
| City | MLOps Engineer | AI Safety Researcher | Difference |
|---|---|---|---|
| San Francisco, CA | $144,519 | $118,871 | MLOps +$25,648 |
| New York, NY | $119,390 | $79,460 | MLOps +$39,930 |
| Seattle, WA | $139,997 | $113,639 | MLOps +$26,358 |
| Austin, TX | $107,475 | $92,366 | MLOps +$15,109 |
| Chicago, IL | $108,552 | $85,872 | MLOps +$22,680 |
| Boston, MA | $133,124 | $107,617 | MLOps +$25,507 |
| London, UK | £79,867 | £63,159 | MLOps +$16,708 |
| Toronto, Canada | CA$130,108 | CA$111,112 | MLOps +$18,996 |
Frequently Asked Questions
Does a MLOps Engineer or AI Safety Researcher earn more?+
A MLOps Engineer earns more on average. The national median salary for a MLOps Engineer is $99,130/year, compared to $81,490/year for a AI Safety Researcher — a difference of $17,640 (22%).
Which has better career growth — MLOps Engineer or AI Safety Researcher?+
MLOps Engineer roles are growing at 52% YoY while AI Safety Researcher demand is growing at 65% YoY. AI Safety Researcher has stronger near-term demand growth.
Can you switch from MLOps Engineer to AI Safety Researcher?+
Yes. Many professionals transition between these roles, especially since both are in the same category. Shared skills include: Python.
Which is harder to automate — MLOps Engineer or AI Safety Researcher?+
AI Safety Researcher has a lower AI automation risk (8% vs 22%). Based on Oxford Martin School and McKinsey 2023 analysis.