Member of Technical Staff, Enterprise Evals Platform
Company websitemercor.com (Mercor, opens in new tab)
Base salary $220K to $425K
Employer-published annual base salary, USD. Advertised base range in San Francisco, CA
You apply on the employer's own site. Six Figure Work does not handle applications.
- LocationSan Francisco, CA
- WorkplaceOn-site
- Status at last checkSource accepting applications when checked
- Last checked (2 h ago) · observed since
About this job
About Mercor
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
Enterprise agents are complex systems, and they only pay off when their work is reliable and economically viable. Evaluation is how you get both: checking correctness is the obvious case, and routing is the subtler one, since choosing a model against cost, latency, and quality requires quality to be measurable at all. Knowing where the bar sits is the hard part. You decompose real work, take the standard from the practitioners who hold it, and encode it so an agent cannot shortcut it.
You will apply what Mercor has learned building benchmarks with domain experts, and devise new methods, so that evals and rubrics keep improving and so do the agents measured against them.
That work only scales with a platform behind it. This is a platform engineering role with good depth of understanding in evals. You will build the verifiers, the environments agents are measured in, and the grading infrastructure that runs at scale, abstracted across customers, domains, and tasks so that every run becomes evidence the next agent inherits instead of starting over.
Read more about how we think about this: Agent Eval Systems
Responsibilities
Define golden sets: decompose real tasks and encode the expert quality bar.
Build verifiers over agent trajectories and outputs, calibrated and hard to game.
Build the eval platform that runs offline environments, task suites, and grading at scale.
Run loss analysis over production trajectories and turn failure modes into regression tests.
Run the optimization loop across models, prompts, skills, and harnesses.
Own the rollout gates that decide whether an agent change ships.
Partner with the Enterprise Platform team and the Applied AI engineers embedded with customers.
What We're Looking For
Professional, academic, or research experience in agent engineering and evaluation, including how agent runtimes and harnesses produce a trajectory and where it fails.
Bachelor’s Degree in Computer Science or a related technical degree such as Electrical Engineering, Physics, Mathematics, Computer Engineering, Information Systems or Statistics
1–5 years in industry level software engineering
Experience building evaluation suites for LLM or agent systems, and familiarity with how benchmarks such as terminal-bench, tau-bench, and APEX are constructed and where they get gamed.
Judgment about task and rubric design: turning a fuzzy notion of quality into something measurable, with agent or model improvements to show for it.
Strong software engineering fundamentals, and the ability to work independently on ambiguous, loosely specified problems.
Bonus: experience with Harbor environments and RL environments.
Why Mercor
Impact: No agent reaches an enterprise customer without clearing the bar you set.
Learning: Eval work spanning frontier model measurement and live enterprise deployments, on production trajectories few teams get to see.
Growth: Research and systems in one role, with fast paths to owning the eval system end to end.
Benefits
Up to $15k relocation bonus
$10K housing bonus (if you live within 0.5 miles of our office)
$1.5K monthly stipend for meals
Generous equity grant vested over 4 years
Free Equinox membership
$200 monthly laundry reimbursement
$200 monthly personal wellness reimbursement
Health, Dental, Vision insurance
Source: Mercor on Ashby · View original posting (opens in new tab)
Pay in context
This role's base floor ($220,000) is above 79% of the 5,063 Software Engineering roles in California listed on this site; 83 others list the same floor. Their median floor is $176,600; the middle half runs from $149,365 to $204,000.
Only jobs on this site count, and each one publishes a base salary of $100,000 or more, so this compares six-figure jobs with each other, not with the whole market. Computed Nov 8, 2026.
This listingMember of Technical Staff, Enterprise Evals Platform$220K to $425K
Software Engineer, Systems & Platform Applied AI$220K to $500K
Brand Design Lead$200K to $300K
Head of Operations Planning$200K to $250K
Research Engineer – Benchmarking$200K to $500K
AI Infrastructure Engineer$200K to $400K
Research Scientist, APEX Benchmarks$200K to $500K
Chart as a table
| Role | Published base |
|---|---|
| Member of Technical Staff, Enterprise Evals Platform (this listing) | $220K - $425K |
| Software Engineer, Systems & Platform Applied AI | $220K - $500K |
| Brand Design Lead | $200K - $300K |
| Head of Operations Planning | $200K - $250K |
| Research Engineer – Benchmarking | $200K - $500K |
| AI Infrastructure Engineer | $200K - $400K |
| Research Scientist, APEX Benchmarks | $200K - $500K |
Observed changes
- Began meeting the published-salary criteria
- First observed on the employer's board
- Observed since
- Employer-stated posting date
- 2026-08-15
Where this pay comes from
Location as the employer wrote it: San Francisco
USD 220,000 – 425,000 · 1 YEAR · Salary
- Method
- Structured pay field published by the employer
- Checked
- Rule set
- us-base-v2
The employer's job board publishes this pay as a dedicated data field. We read the field directly; no interpretation was needed.
What we verified
- The pay figures come from the employer's own listing, quoted exactly above.
- The pay is an annual base salary in US dollars with a minimum of at least $100,000.
- The role was publicly listed on the employer's job board when last checked.
We do not verify what the employer will actually offer, whether the employer is still recruiting for the role, or anything beyond what the employer published. Full methodology.
