Machine Learning Engineer, Marketplace

About Mercor

Mercor is defining the future of work. 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 cant be captured in code alone. Today more than 30000 experts in our network collectively earn over $2 million a day.

Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious fast-paced and deeply committed team. Youll 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 Mercors 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 Youll 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 Were 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

• Generous equity grant vested over 4 years

• A $20K relocation bonus (if moving to the Bay Area)

• A $10K housing bonus (if you live within 0.5 miles of our office)

• A $1.5K monthly stipend for meals

• Free Equinox membership

• Health insurance

Required Experience:

IC