$197,500
median · per year, annualized USD
$191,498
median · per year, annualized USD
Machine Learning Engineer, Speech LLM Training - San Francisco
Machine Learning Engineer, Model Evaluations (Speech LLM) - San Francisco
Principal Machine Learning Engineer- LLM Fine-tuning and Optimization
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
Staff Machine Learning Engineer, CustomerLake (ML/LLM)
Senior Software Engineer, Growth
Intermediate Backend Software Engineer
Staff Software Engineer (Backend)
Lead Software Engineer (Backend)
Senior Software Engineer, Machine Learning (Audio)
Software Engineer - Solana
Software Engineer - EVM
Software Engineer - Stellar
Software Engineer - Compliance
Senior Staff+ Software Engineer, Kubernetes Platform
| Company | Median | Roles |
|---|---|---|
Anduril Industries
|
$222,000 | 296 |
OpenAI
|
$309,000 | 111 |
Speechify
|
$170,000 | 103 |
Databricks
|
$214,700 | 98 |
Roblox
|
$269,270 | 82 |
Applied Intuition
|
$179,250 | 82 |
Palantir
|
$167,500 | 77 |
SpaceX
|
$137,500 | 65 |
| Location | Median | Roles |
|---|---|---|
| San Francisco | $250,000 | 7 |
| New York | $197,500 | 3 |
| Location | Median | Roles |
|---|---|---|
| San Francisco | $225,000 | 1,398 |
| Sydney | $224,060 | 8 |
| New York | $215,000 | 1,166 |
| Seattle | $211,500 | 417 |
| Austin | $198,900 | 135 |
| Boston | $186,050 | 229 |
| Los Angeles | $182,825 | 166 |
| Bangalore | $181,219 | 4 |
| Chicago | $180,000 | 137 |
| London | $175,072 | 113 |
The median for Prompt Engineer is $197,500 per year (typically $158,813–$241,325), versus $191,498 for Software Engineer ($152,500–$225,000). That puts Prompt Engineer about 3% ahead at the median.
Both figures are computed live from active listings on JobsRadar and normalized to annualized USD, so Prompt Engineer and Software Engineer 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.