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
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Ranking and matching systems that determine which candidates and opportunities are surfaced
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Models for recommendation, personalization, and marketplace optimization
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Retrieval, scoring, and decision pipelines operating at global scale
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Feedback loops that learn from downstream hiring outcomes, not just top-of-funnel engagement
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Real-time and batch inference systems embedded in product-critical workflows
Example Problems
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Improve candidate-job matching using embeddings, structured attributes, and behavioral signals
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Optimize ranking toward long-term hiring outcomes under delayed and incomplete labels
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Design models that balance marketplace objectives such as fill rate, quality, speed, and conversion
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Build systems for candidate allocation, opportunity routing, and liquidity optimization
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Develop evaluation and experimentation frameworks that connect model performance to business results
What We’re Looking For
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Strong track record of shipping ML systems into production
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Experience with ranking, recommendation, search, matching, or marketplace problems
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Good judgment on model design, objective functions, evaluation, and tradeoffs
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Comfort working across the full applied ML stack: data, features, training, inference, and iteration
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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.