$211,910
median · per year, annualized USD
$189,000
median · per year, annualized USD
Senior Software Engineer, Machine Learning (Audio)
Research Engineer, Machine Learning (Reinforcement Learning)
Research Engineer, Machine Learning (RL Velocity)
Software Engineer, ML Networking
Research Engineer, Machine Learning (Reinforcement Learning)
Research Engineer, Machine Learning (RL Velocity)
Machine Learning Engineer, Integrity
Principal Machine Learning Engineer, Ads Delivery
Staff Machine Learning Engineer, Ad Serving
Senior Machine Learning Engineer, Search
Senior Machine Learning Engineer, DevOps/SRE
Staff Machine Learning Operations Engineer
Staff Engineer - ML Infra / MLOps
Staff ML Ops Engineer
Lead Software Platform Engineer, MLOps
Senior ML Ops Engineer (Machine Learning Infrastructure)
MLOps Engineer
MLOps Engineer (JAX, PyTorch, Pallas/Triton)
ML Ops Infrastructure Engineer
Senior Machine Learning Engineer, Operations Research
| Company | Median | Roles |
|---|---|---|
Nuro
|
$242,540 | 16 |
Torc Robotics
|
$195,050 | 16 |
Reddit
|
$276,000 | 14 |
Pinterest
|
$260,867 | 13 |
Airbnb
|
$238,500 | 13 |
Roku
|
$294,750 | 12 |
DoorDash USA
|
$169,350 | 12 |
Roblox
|
$311,785 | 11 |
| Company | Median | Roles |
|---|---|---|
Torc Robotics
|
$142,650 | 2 |
Garner Health
|
$324,500 | 1 |
Roku
|
$254,875 | 1 |
Quince
|
$251,500 | 1 |
LVT
|
$242,650 | 1 |
TetraScience
|
$235,000 | 1 |
| AM Atomic Machines | $225,000 | 1 |
Instacart
|
$212,750 | 1 |
| Location | Median | Roles |
|---|---|---|
| Chicago | $275,000 | 5 |
| Singapore | $260,000 | 3 |
| San Francisco | $249,410 | 160 |
| Austin | $235,000 | 9 |
| Boston | $233,750 | 20 |
| Los Angeles | $230,000 | 14 |
| New York | $228,000 | 94 |
| Seattle | $220,300 | 47 |
| London | $195,018 | 15 |
| Denver | $188,750 | 6 |
The median for ML Engineer is $211,910 per year (typically $175,000–$255,700), versus $189,000 for MLOps ($152,900–$225,000). That puts ML Engineer about 12% ahead at the median.
Both figures are computed live from active listings on JobsRadar and normalized to annualized USD, so ML 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.