$198,750
median · per year, annualized USD
$200,000
median · per year, annualized USD
Machine Learning Engineer, Speech LLM Training - San Francisco
Principal Machine Learning Engineer- LLM Fine-tuning and Optimization
Machine Learning Engineer, Model Evaluations (Speech LLM) - San Francisco
Machine Learning Engineer, Inference & Serving (Speech LLM) - San Francisco
Machine Learning Engineer, LLM Evals & Observability
Machine Learning Engineer, LLM Evals & Observability
Principal LLM Inference Engineer
Staff Attack Engineer, AI/LLM
Staff ML/LLM Ops Engineer
Software Engineer, Artificial Intelligence/LLM (Multiple Seniority Levels)
Lead Safety Engineer, Robotics
Machine Learning Engineer, Distributed Data Systems - Robotics
Firmware Engineer, Robotics
Software Engineer, Distributed Data Systems - Robotics
Electrical Engineer, Robotics
Inference Engineer, Robotics
Staff Robotics Engineer / Tech Lead – Whole-Body Control & Robot Learning
Software Engineer: Robotics Controls
Software Engineer: ML Robotics Systems
Staff Robotics Software Engineer, Air Vehicle Autonomy
| Company | Median | Roles |
|---|---|---|
Anduril Industries
|
$222,000 | 53 |
Field Ai
|
$185,000 | 9 |
OpenAI
|
$370,000 | 7 |
| SA Skild AI | $200,000 | 7 |
Neuralink
|
$155,750 | 6 |
DoorDash USA
|
$207,500 | 5 |
Intrinsic
|
$174,050 | 5 |
Agility Robotics
|
$215,750 | 4 |
| Location | Median | Roles |
|---|---|---|
| San Francisco | $250,000 | 7 |
| New York | $197,500 | 3 |
| Location | Median | Roles |
|---|---|---|
| Boston | $222,000 | 13 |
| New York | $214,750 | 4 |
| Seattle | $214,750 | 6 |
| Austin | $212,000 | 9 |
| San Francisco | $206,000 | 36 |
| Denver | $150,000 | 3 |
| Los Angeles | $131,250 | 6 |
The median for Prompt Engineer is $198,750 per year (typically $180,000–$243,238), versus $200,000 for Robotics ($170,250–$222,000). That puts Robotics about 1% ahead at the median.
Both figures are computed live from active listings on JobsRadar and normalized to annualized USD, so Prompt Engineer and Robotics 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.