Machine Learning Engineer

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. Today, more than 30,000 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. 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.

As a Machine Learning Engineer at Mercor, you’ll operate at the intersection of backend engineering and applied machine learning. ML Engineers at Mercor are generalists first, shipping production systems that power performance prediction, search, recommendation, and fraud detection while also bringing statistical and modeling rigor where it matters. The work spans everything from building APIs and infrastructure to training and deploying models, always tied closely to core product outcomes. You’ll collaborate with product engineers and operations to deliver systems that directly impact how companies source talent and how candidates find opportunities

• Research, train, and productionize ML models for engagement prediction, scoring, search

• Build backend infrastructure and APIs to serve ML models reliably at scale.

• Run experiments, analyze results, and iterate quickly to improve both models and product performance.

• Work cross-functionally with Operations and Product to translate business needs into model-driven solutions.

• Wear many hats: from backend engineer to applied ML practitioner to product problem-solver.

• Strong backend engineering skills (ex. Python/Django or similar) plus a solid foundation in applied ML and statistics.

• Proven experience shipping production systems or ML-driven products end-to-end.

• High ownership and comfort operating in ambiguous, fast-changing environments.

• Generalist mindset: willing to flex between backend, modeling, data pipelines, and product problem-solving.

• Impact: Your work powers how the world’s leading AI labs train and test their models.

• Learning: Get early insights into frontier model capabilities months before the market.

• Growth: Work on both infrastructure and research-adjacent projects with fast paths to ownership.