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Über
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 Bi-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
Sprachkenntnisse
- English
Hinweis für Nutzer
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