$200,455
median · per year, annualized USD
$187,500
median · per year, annualized USD
Senior Software Engineer, Machine Learning (Audio)
Senior/Staff Backend Engineer, Applied AI
Senior/Staff Full Stack Engineer, Applied AI
Machine Learning Engineer - Fraud Risk
Backend Engineer, AI
Research Engineer, Machine Learning (RL Velocity)
Research Engineer, Machine Learning (Reinforcement Learning)
Research Engineer, Machine Learning (RL Velocity)
Research Engineer, Machine Learning (Reinforcement Learning)
Senior AI Security Engineer II
Senior Machine Learning Engineer, DevOps/SRE
Staff Machine Learning Operations Engineer
Senior Data Engineer, MLOps [Remote-US]
Senior, ML Engineer - ML Ops Framework
Senior, ML Engineer - ML Ops Framework
Senior Machine Learning Operations Engineer
Staff ML/LLM Ops Engineer
Sr. Machine Learning Ops Engineer
DoD MLOps Software Engineer
Sr. Machine Learning Ops Engineer
| Company | Median | Roles |
|---|---|---|
OpenAI
|
$239,000 | 37 |
Mindrift
|
$312,000 | 31 |
Handshake
|
$252,500 | 22 |
Databricks
|
$224,163 | 22 |
Motional
|
$200,500 | 21 |
Pinterest
|
$265,928 | 20 |
Reddit
|
$276,000 | 19 |
Applied Intuition
|
$173,500 | 18 |
| Company | Median | Roles |
|---|---|---|
Garner Health
|
$297,500 | 2 |
Torc Robotics
|
$249,000 | 2 |
Quanata
|
$256,500 | 1 |
Roku
|
$254,875 | 1 |
LVT
|
$242,650 | 1 |
Hayden AI
|
$230,000 | 1 |
Instacart
|
$212,750 | 1 |
Cimgroup
|
$200,000 | 1 |
| Location | Median | Roles |
|---|---|---|
| Singapore | $250,000 | 4 |
| San Francisco | $229,756 | 466 |
| New York | $220,000 | 350 |
| Seattle | $205,500 | 97 |
| Los Angeles | $200,000 | 33 |
| Chicago | $197,500 | 36 |
| Austin | $196,000 | 44 |
| Bangalore | $194,838 | 3 |
| Sydney | $190,000 | 3 |
| Toronto | $187,500 | 59 |
The median for AI Engineer is $200,455 per year (typically $165,350–$243,750), versus $187,500 for MLOps ($147,500–$236,325). That puts AI Engineer about 7% ahead at the median.
Both figures are computed live from active listings on JobsRadar and normalized to annualized USD, so AI Engineer and MLOps are compared on equal terms regardless of the currency each role was originally posted in. Only roles that publish a salary range feed the medians; numbers refresh every few hours as new roles post and older ones close, so this comparison reflects the market right now rather than a fixed survey. Use it as a directional benchmark when weighing one path against the other.