Tech Lead, Applied AI - Evals
Mercor is hiring a Tech Lead, Applied AI - Evals in San Francisco, United States. It pays $260k-$500k a year and Level rates it ; you can apply on Mercor.
Level's read on this job
- On Level's scale, this role is Builds AI ●●●●: the job is building AI systems.
- Level classifies it as a lead role.
- Level lists 85 other open roles at Mercor.
AI in this role
Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.
Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.
About the Role
Mercor’s evals product helps frontier labs and enterprises understand how their models perform on real work. Labs use it to decide what to train next, and enterprises use it to decide which models and agents to trust. It runs evals at scale, grades every result and explains why a model fails. The team also runs APEX, Mercor's family of benchmarks built with domain experts to measure how well frontier models do professional work in fields like law, finance and consulting. Its public leaderboard ranks the latest models, and its results appear in model cards across the industry.
As the Tech Lead on the Evals team, you will own the evals product end to end. You will decide what the team builds next and stay hands-on in the code. The hard problems are in grading open-ended professional work reliably, keeping results reproducible as models and tasks change, and running long agent tasks at scale. The work is becoming as much a research problem as an engineering one.
This role grows with the platform, from evals into post-training and new product areas. You will help turn the failures we find into data that improves models.
What You'll Do
Own the evals flywheel and its direction: Set the architecture and roadmap, and make the tradeoff calls in design reviews.
Lead the product customers use to evaluate models: Ship the APIs, data models and UI that labs and enterprises use to run evals and compare models.
Power APEX launches: Build the systems that run every new frontier model on APEX and publish correct, defensible results to the public leaderboard.
Explain why models fail: Build failure analysis that links each failure pattern to the exact steps that went wrong.
Set the quality bar for evals: Calibrate graders against expert judgment and decide when a grader or agent change is ready to ship.
Keep production healthy: Make sure the product stays reliable and its compute costs stay in check.
Work directly with customers: Lead pilots with labs and enterprises and bring what you learn back into the roadmap.
Grow the team: Hire and mentor engineers, and manage the people on the team.
What We Are Looking For
Bachelor's Degree in Computer Science or a related field
5+ years building and running backend systems in production, with time at senior or staff level
Strong full-stack skills in a modern backend language (Python, Go or TypeScript) and a frontend framework such as React
A track record of owning a platform or product area end to end, including leading the team that built it
Strong product sense that starts from what the user needs
Experience building data-heavy products: data models, APIs and views over large volumes of results
Excellent written and verbal communication with engineers, researchers and customers
Comfort with ambiguity and a habit of shipping iteratively
Benefits
Bi-annual performance bonus structure
Generous equity grant vested over 4 years
Up to $15k Relocation bonus
$10K housing bonus (if you live within 0.5 miles of our office)
$1.5K monthly stipend for meals
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
Health, Dental, Vision insurance
How we rate this
Tech Lead, Applied AI - Evals at Mercor rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
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