Software Engineer, Machine Learning

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