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

Requirements

Must-haves

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
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
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
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.
Python, Go, embeddings, fine-tuning, RAG, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlpythongopostgreskubernetes

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

Wellness