{"id":3988404,"url":"https://alion.io/job/mercor-software-engineer-applied-ai-evals","title":"Software Engineer, Applied AI- Evals","company":{"id":4960,"name":"Mercor","domain":"mercor.com","url":"https://alion.io/company/mercor","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":true,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":85,"open_postings":12,"ghost_share":0,"stale_share":0.417,"repost_share":0,"time_to_fill_p50_days":70,"computed_at":"2026-11-09T06:03:00Z"}},"role":"Backend","role_family":"Backend","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":220000,"max":250000,"currency":"USD","period":"year","gross":null,"usd_annual":250000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":true,"has_equity":true,"technologies":[{"name":"Go","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"Post-training","optional":false},{"name":"Python","optional":false},{"name":"React.js","optional":false},{"name":"TypeScript","optional":false},{"name":"JavaScript","optional":true}],"status":"live","first_seen_at":"2026-11-09T22:26:24Z","employer_posted_date":"2026-11-09","last_verified_at":"2026-11-09T22:28:22Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"About Mercor\nMercor'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.\nMercor 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.\nAbout the Role\nMercor’s evals platform 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.\nAs a Software Engineer on the Evals product team, you will build the scorecards, failure reports and dataset views that help customers turn eval runs into decisions. You will solve hard problems in scale, grading accuracy and reproducibility, while keeping the product simple to use.\nThis 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.\nWhat You'll Do\nBuild the product customers use to evaluate models: Ship the APIs, data models and UI that help customers run evals, compare models and share scores.\n\nOwn APEX launches: Run every new frontier model on APEX and publish correct, defensible results to the public leaderboard.\n\nExplain why models fail: Build failure analysis that helps customers see where their models fail, with each pattern linked to the exact steps that went wrong.\n\nMake benchmarks easy to find and manage: Build dataset views, registries and versioning that help customers find the right benchmark or bring their own.\n\nTake customers from first login to self-serve: Build onboarding, roles, key management and usage views that help new teams run evals on their own.\n\nWork directly with customers: Join pilots with labs and enterprises and turn what they need into the roadmap.\n\nWhat We Are Looking For\nBachelor's Degree in Computer Science or a related field\n\nStrong full-stack skills in a modern backend language (Python, Go or TypeScript) and a frontend framework such as React\n\nA track record of owning product features end to end, from idea to production\n\nStrong product sense that starts from what the user needs\n\nExperience building data-heavy products: data models, APIs and views over large volumes of results\n\nExcellent written and verbal communication with engineers, researchers and customers\n\nComfort with ambiguity and a habit of shipping iteratively\n\nExperience with LLM evals, benchmarks or AI developer tools is a plus\n\nStartup experience or experience building B2B developer products is a plus\n\nBenefits\nBi-annual performance bonus structure\n\nGenerous equity grant vested over 4 years\n\nUp to $15k Relocation bonus\n\n$10K housing bonus (if you live within 0.5 miles of our office)\n\n$1.5K monthly stipend for meals\n\nFree Equinox membership\n\n$200 monthly laundry 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