$170,000
median · per year, annualized USD
$189,000
median · per year, annualized USD
Staff+ Research Engineer, RL Data Platform
Data Infrastructure Engineer, Pre-training
Data Engineer
Senior Data Engineer
Staff / Principal Data Engineer
Software Engineer - Data Aquisition (systems)
Software Engineer, Data Acquisition
Senior Staff Backline Engineer - Data & AI
Machine Learning Engineer, Distributed Data Systems - Robotics
Software Engineer, Frontier Data Products
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 |
|---|---|---|
Speechify
|
$170,000 | 102 |
Mindrift
|
$83,200 | 32 |
Accenture Federal Services
|
$144,250 | 27 |
Olsson
|
$121,500 | 21 |
OpenAI
|
$315,000 | 17 |
Esri
|
$113,360 | 15 |
Anduril Industries
|
$181,500 | 14 |
Anthropic
|
$362,500 | 13 |
| 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 |
|---|---|---|
| San Francisco | $217,500 | 166 |
| New York | $213,281 | 168 |
| Seattle | $203,750 | 42 |
| Boston | $185,000 | 29 |
| Los Angeles | $175,000 | 21 |
| Chicago | $175,000 | 34 |
| Austin | $168,935 | 17 |
| Denver | $150,000 | 28 |
| Atlanta | $145,163 | 12 |
| London | $144,187 | 11 |
The median for Data Engineer is $170,000 per year (typically $143,125–$215,000), versus $189,000 for MLOps ($152,900–$225,000). That puts MLOps about 11% ahead at the median.
Both figures are computed live from active listings on JobsRadar and normalized to annualized USD, so Data 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.