Machine Learning Engineer, Marketplace
Role Overview
This senior-level Machine Learning Engineer role on the growth team involves building and maintaining high-throughput systems for Mercor's hiring engine, including ranking pipelines, global search infrastructure, and production ML for personalization. You will work on distributed backends and ML infrastructure, focusing on real-time data processing and reliability to directly impact revenue through scalable matching and conversion systems.
Perks & Benefits
The role is fully remote, likely with flexible hours but may require alignment with team time zones for collaboration. Benefits include generous equity, health insurance, a monthly meal stipend, and a free Equinox membership, with relocation and housing bonuses if moving near an office. The culture is fast-paced and ambitious, offering career growth through work with leading AI companies and a focus on high-impact, revenue-driven projects.
Full Job Description
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 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.
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
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
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