$160,000
median · per year, annualized USD
$163,800
median · per year, annualized USD
Staff+ Research Engineer, RL Data Platform
Pre-training Data Infrastructure Engineer
Staff / Principal Data Engineer
Software Engineer - Data Aquisition (systems)
Software Engineer, Data Acquisition
Software Engineer, Frontier Data Products
Data Platform Engineer
Data Engineer
Software Engineer, Search Systems - Code Data
Research Engineer - Environments, Data and Post-Training
Staff Machine Learning Operations Engineer
Machine Learning Engineer/Senior Machine Learning Engineer - Devops, AI for Drug Discovery
Principal Machine Learning Operations Engineer
Lead ML Engineer - Advance AI (applied ML, LLM's, ML Ops)
Senior / Staff ML Ops Engineer
Lead Software Platform Engineer, MLOps
Staff MLOps Engineer
Software Engineer - ML Ops (R37)
Senior Staff ML Ops Engineer
Matterport - Senior ML Ops Engineer
| Company | Median | Roles |
|---|---|---|
Speechify
|
$170,000 | 144 |
Booz Allen Hamilton
|
$126,800 | 81 |
Accenture Federal Services
|
$149,800 | 28 |
CACI
|
$139,950 | 25 |
Olsson
|
$120,000 | 21 |
Leidos
|
$151,475 | 19 |
Northrop Grumman
|
$127,350 | 18 |
Blue Origin
|
$188,162 | 17 |
| Company | Median | Roles |
|---|---|---|
Weekday AI
|
$202,800 | 2 |
Target
|
$197,000 | 2 |
Workday
|
$150,400 | 2 |
RELX
|
$140,450 | 2 |
Booz Allen Hamilton
|
$126,300 | 2 |
Shirley Ryan AbilityLab
|
$88,225 | 2 |
Garner Health
|
$324,500 | 1 |
AiDASH, Inc.
|
$250,000 | 1 |
| Location | Median | Roles |
|---|---|---|
| San Francisco | $211,000 | 192 |
| Seattle | $196,700 | 69 |
| New York | $192,736 | 213 |
| Los Angeles | $180,350 | 33 |
| Boston | $173,750 | 53 |
| Singapore | $165,000 | 5 |
| Austin | $161,450 | 51 |
| Chicago | $154,600 | 90 |
| Denver | $153,300 | 56 |
| Atlanta | $153,300 | 35 |
The median for Data Engineer is $160,000 per year (typically $127,394–$198,009), versus $163,800 for MLOps ($137,000–$193,500). That puts MLOps about 2% 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.