ML Evaluation Engineer
Impact: Model safety, reliability, and quality assurance before and after production deployment
Design and build evaluation frameworks that measure the quality, safety, and reliability of machine learning models. Create benchmark datasets, automated evaluation pipelines, and human evaluation workflows that give teams confidence in model behaviour before and after deployment.
What the day looks like
- People interaction
- Moderate
- Team vs solo
- 55% Team / 45% Solo
- Client facing
- Rarely
- Impact visibility
- Moderate
- Travel
- Minimal
- Schedule flexibility
- Flexible
- Remote work
- Mostly Remote
- Typical work hours
- 40 to 50 hours/week
- Stress level
- Moderate
At a glance
- Median salary
- $150,000
- Entry-level
- $105,000 - $130,000
- Senior
- $200,000+
- Growth by 2033
- 28% (much faster than average)
- Demand
- Growing Fast
- Freelance potential
- Low
- Salary growth potential
- High - 65 to 80% growth from entry to senior
- Typical student debt
- $20,000 - $60,000
Skills you'll use
Hard skills
- Python
- Statistical analysis
- Benchmark design
- SQL
- LLM evaluation frameworks
- Data annotation
Soft skills
- Analytical thinking
- Attention to detail
- Intellectual curiosity
- Collaboration
- Critical thinking
Technical complexity: High
How to get there
- Minimum education
- Bachelor's Degree
- Licensing
- No
- Years to mid-career
- 2 to 4 years
- Years to senior
- 5 to 8 years
- Career switching
- Moderate
Where this career leads
How people arrive here
Where you can go from here
Typical progression
- ML Engineer > ML Evaluation Engineer > Senior Evaluation Engineer > Staff Evaluation Scientist > Principal Evaluation Scientist
Future outlook
- Automation probability
- 20% moderate risk as some evaluation tasks can be automated with LLM judges
- AI disruption risk
- Moderate
- Demand trend
- Growing Fast
How people feel about it
- Overall satisfaction
- 7.8/10
- Meaning
- 8/10
- Work-life balance
- 7.2/10
- Prestige
- 7.7/10
- Social perception
- High