Machine Learning Engineer, Frontier Data Products

Навыки

• AWS
• Отладка и поиск ошибок
• Дообучение моделей
• LLM
• Machine Learning
• Ответственность за результат
• PostgreSQL

О компании и продукте

• 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.
• Frontier AI companies are increasingly bottlenecked on expert judgment — capturing it reliably, validating it at scale, and turning it into durable model behavior. This role sits at the center of that problem.

Задачи

• Build ML systems that score, validate, and improve complex work products where correctness is nuanced and labels are imperfect
• Design evaluation frameworks for ambiguous tasks where ground truth is partial, delayed, or disputed
• Build feedback loops that turn review, disagreement, correction, and adjudication into measurable model and system improvements
• Own production ML behavior end-to-end: precision/recall tradeoffs, regression detection, drift, latency, cost, and explainability
• Improve model quality using the right tool for the job — prompting, fine-tuning, retrieval, active learning, heuristics, and error analysis
• Partner with backend engineers to integrate inference into durable, long-running workflows without sacrificing debuggability or human oversight
• What Makes This Role Different
• The architecture is not set — early engineers will define how quality is measured, how models and humans interact, where automation is trusted, and how the system compounds over time
• The feedback loop is short: shipping a model behavior change directly and visibly affects what customers receive
• You're working on a strategically central product area at Mercor at a moment when frontier AI companies have no good solution to the problem you're solving
• Moving fast on a young, high-ownership codebase where your decisions have long-term architectural weight
• Operating across models, data, backend systems, and product surfaces — context switching is the default, not the exception
• Debugging production ML failures in live, long-running workflows where silent errors matter
• Working closely with backend engineers on a stack of Python, Temporal, Postgres, AWS, and LiteLLM
• Balancing automation confidence with human review — knowing when to defer is as important as knowing when to ship

Требования

• Track record of shipping ML systems that improved a real product, workflow, or business metric
• Strong instincts for model quality, evaluation design, error analysis, and production failure modes
• Comfort operating in ambiguous problem spaces where labels are imperfect and correctness evolves
• Sound judgment about when to reach for prompting, fine-tuning, heuristics, retrieval, human review, or a simpler product constraint
• Solid engineering fundamentals across the full ML stack — not just modeling
• Familiarity with LLM applications, model-assisted workflows, evaluation frameworks, or human-in-the-loop ML is a strong plus
• You're likely someone who
• Defaults to simple, inspectable ML systems that improve quickly and fail in understandable ways — not the most impressive architecture
• Gets uncomfortable when a model ships without a clear evaluation story
• Can hold ambiguity without paralysis and make reasonable bets with incomplete information
• Cares about the real-world output of the system, not just the benchmark

Условия

• 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

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