About Mercor

Job description

# **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 As a Machine Learning Engineer on the Marketplace team, you will build the models and decision systems that power Mercor's hiring engine. This includes search and ranking, candidate-job matching, marketplace recommendations, personalization, and allocation decisions across a rapidly growing talent network. This is an applied ML role with direct product and revenue impact. You will work on problems shaped by real marketplace constraints: sparse and delayed labels, cold start, noisy feedback, heterogeneous supply and demand, and the need to optimize across speed, quality, and conversion simultaneously. ## What You'll Build - Ranking and matching systems that determine which candidates and opportunities are surfaced - Models for recommendation, personalization, and marketplace optimization - Retrieval, scoring, and decision pipelines operating at global scale - Feedback loops that learn from downstream hiring outcomes, not just top-of-funnel engagement - Real-time and batch inference systems embedded in product-critical workflows ## Example Problems - Improve candidate-job matching using embeddings, structured attributes, and behavioral signals - Optimize ranking toward long-term hiring outcomes under delayed and incomplete labels - Design models that balance marketplace objectives such as fill rate, quality, speed, and conversion - Build systems for candidate allocation, opportunity routing, and liquidity optimization - Develop evaluation and experimentation frameworks that connect model performance to business results ## What We're Looking For - Strong track record of shipping ML systems into production - Experience with ranking, recommendation, search, matching, or marketplace problems - Good judgment on model design, objective functions, evaluation, and tradeoffs - Comfort working across the full applied ML stack: data, features, training, inference, and iteration - Strong engineering fundamentals and a bias toward simple, robust systems ## Why This Role This role sits on a core decision layer of the product. Your work will directly shape how talent is discovered, matched, and hired, and will influence fundamental marketplace outcomes across quality, speed, and revenue. ## Tech Stack Python, Go, embeddings, fine-tuning, RAG, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform ## **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 Compensation Range: $130K - $500K