data
Posted 2 hours agoMachine Learning Engineer, Marketplace
at Mercor
San Francisco, United StatesOn-site
Responsibilities
- 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
Requirements
- ABOUT MERCOR Mercor's mission is to organize human intelligence to power the AI economy.
- We partner with leading AI labs and enterprises to provide the human intelligence essential to AI development.
- Our vast talent network trains frontier AI models in the same way teachers teach students: by sharing knowledge, experience, and context that can't be captured in code alone.
- Mercor is creating a new category of work where expertise powers AI advancement.
- You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society.
- 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 is an applied ML role with direct product and revenue impact.
- Strong track record of shipping ML systems into production
- Experience with ranking, recommendation, search, matching, or marketplace problems
- 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