Mercor
Machine Learning Engineer, Frontier Data Products
About this role
Mercor is seeking a Machine Learning Engineer to enhance its hiring engine through the development of advanced models for candidate-job matching, recommendations, and marketplace optimization. This role is pivotal in addressing challenges like sparse data and improving overall hiring outcomes in a fast-paced environment.
What you'll do
- Develop ranking and matching systems for candidates and job opportunities
- Create models for recommendation and personalization
- Design pipelines for retrieval, scoring, and decision-making at scale
- Implement feedback loops from hiring outcomes
- Optimize models for fill rate, quality, and conversion
- Establish evaluation frameworks linking model performance to business results
What they're looking for
- Experience deploying ML systems in production
- Background in ranking, recommendation, and search algorithms
- Strong judgment in model design and evaluation
- Full-stack applied ML proficiency
- Engineering fundamentals and system design simplicity
- Comfort with a variety of technologies including Python and Go
- Ability to handle noisy data and model trade-offs
- Expertise in marketplace-related problem-solving
Benefits
- Bi-annual performance bonuses
- Equity grant vested over 4 years
- Relocation assistance up to $15,000
- Monthly housing bonus if living nearby
- $1,500 meal stipend
- Free Equinox gym membership
- $200 monthly laundry reimbursement
- $200 monthly wellness reimbursement
- Health, dental, and vision insurance
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Mercor
Mercor builds a marketplace platform connecting expert talent to AI opportunities, supported by identity infrastructure, matching algorithms, and internal tools for data management. The company is hiring Software Engineers, Machine Learning Engineers, Fullstack Engineers, and Security Engineers to develop backend systems, ML models, cloud infrastructure, and distributed platforms.
- Website
- mercor.io
Likely interview questions
- Tell us about a time you shipped an ML system to production that directly improved a product metric or user outcome. What was the biggest surprise or failure mode you encountered?
- How would you approach building an evaluation framework for a task where ground truth is ambiguous, labels are noisy, or correctness is subjective?