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 $3 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 RoleAs 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 BuildRanking 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
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
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
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 StackPython, Go, embeddings, fine-tuning, RAG, Kafka, Postgres, Redis, Elasticsearch, Kubernetes, Terraform
BenefitsBi-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
Skills Required
- Proven track record 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 across the applied ML stack: data, features, training, inference, and iteration
- Experience building real-time and batch inference systems
- Experience with embeddings, fine-tuning, and RAG approaches
- Experience with evaluation and experimentation frameworks tied to business metrics
- Strong engineering fundamentals and bias toward simple, robust systems
- Familiarity with Kafka, Postgres, Redis, Elasticsearch, Kubernetes, and Terraform
- Proficiency in Python and Go
Mercor Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Mercor and has not been reviewed or approved by Mercor.
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Fair & Transparent Compensation — Pay is considered competitive across many roles, with clear hourly ranges and an hourly/pay‑per‑task mix designed to align rates with expertise. The structure emphasizes transparent, appropriate pay levels and guarantees payment for legitimate logged time.
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Strong & Reliable Incentives — Payments are processed on a predictable weekly cadence via Stripe/Wise, and some tracks offer additional weekly bonus incentives for top performers. This combination of regular payouts and performance bonuses supports dependable earnings when projects are active.
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Equity Value & Accessibility — Select full‑time roles include generous equity grants alongside cash perks such as relocation and housing bonuses. These elements increase total compensation for those positions.
Mercor Insights
What We Do
We use AI to understand human ability and match talent with the opportunities they're best suited for.
