National median base salary of $168,000, with a typical range of $110,000 to $350,000 depending on level, metro and industry.
AI and machine-learning engineers in the United States typically earn a base salary between $110,000 and $350,000, with a national median near $168,000 — the highest of the mainstream engineering tracks. Entry-level AI engineers commonly start between $110,000 and $145,000; mid-level engineers earn about $172,000; senior engineers cluster near $225,000; and staff or research engineers reach $285,000 and above before equity.
Base salary bands by seniority. Ranges are national; see the metro table below for location adjustments.
| Level | Experience | Range | Median |
|---|---|---|---|
| Entry AI / ML Engineer | 0–2 years | $110K – $145K | $128,000 |
| Mid-level AI / ML Engineer | 2–5 years | $145K – $200K | $172,000 |
| Senior AI / ML Engineer | 5–8 years | $190K – $265K | $225,000 |
| Staff / Research Engineer | 8+ years | $240K – $350K | $285,000 |
Indicative US base-salary ranges for 2026, compiled from publicly posted pay ranges and published industry salary surveys. Figures are base salary only — they exclude bonus, equity and benefits — and actual pay varies by employer, metro, industry and individual experience.
Cost of labour is the largest single source of pay variance. These figures apply each metro's adjustment to the $168,000 national median.
| Metro | vs national | Adjusted median |
|---|---|---|
| San Francisco Bay Area, CA | +28% | $215,000 |
| Seattle, WA | +20% | $201,500 |
| New York, NY | +18% | $198,000 |
| Boston, MA | +12% | $188,000 |
| Los Angeles, CA | +10% | $185,000 |
| Washington, DC | +8% | $181,500 |
| Austin, TX | +6% | $178,000 |
| Chicago, IL | +4% | $174,500 |
| Denver, CO | +2% | $171,500 |
| Dallas–Fort Worth, TX | +1% | $169,500 |
| Atlanta, GA | -3% | $163,000 |
| Phoenix, AZ | -5% | $159,500 |
AI engineering has the fastest-growing posting volume and the widest pay dispersion of any engineering track. The largest premiums attach to engineers who have shipped models into production under real latency and cost constraints, rather than to model experimentation alone.
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