AI Benchmark Engineer
Impact: Scientific progress measurement and accountability for AI capability claims
Design, build, and maintain benchmark suites that measure AI model capabilities across reasoning, knowledge, safety, and domain-specific tasks. Create reproducible evaluation frameworks that the research community uses to track progress and compare models.
What the day looks like
- People interaction
- Moderate
- Team vs solo
- 50% Team / 50% Solo
- Client facing
- Rarely
- Impact visibility
- High
- Travel
- 5 to 10% for conferences
- Schedule flexibility
- Flexible
- Remote work
- Mostly Remote
- Typical work hours
- 40 to 50 hours/week
- Stress level
- Moderate
At a glance
- Median salary
- $155,000
- Entry-level
- $110,000 - $135,000
- Senior
- $205,000+
- Growth by 2033
- 25% (faster than average)
- Demand
- Growing Fast
- Freelance potential
- Low
- Salary growth potential
- High - 65 to 80% growth from entry to senior
- Typical student debt
- $30,000 - $80,000
Skills you'll use
Hard skills
- Python
- Statistical analysis
- Benchmark design
- LLM evaluation
- Dataset curation
- Research methodology
Soft skills
- Analytical thinking
- Intellectual curiosity
- Attention to detail
- Critical thinking
- Communication
Technical complexity: Very High
How to get there
- Minimum education
- Master's Degree
- Licensing
- No
- Years to mid-career
- 3 to 5 years
- Years to senior
- 6 to 9 years
- Career switching
- Hard
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- ML Engineer > AI Benchmark Engineer > Senior Benchmark Engineer > Staff Evaluation Scientist > Principal Scientist
Future outlook
- Automation probability
- 15% low risk as benchmark design requires deep understanding of capability gaps
- AI disruption risk
- Moderate
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 8/10
- Meaning
- 8.3/10
- Work-life balance
- 7.2/10
- Prestige
- 7.9/10
- Social perception
- High