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This listing was closed on Aug 18, 2026.
Browse Open JobsMachine Learning Engineer, Marketplace
Summary by AIEngineer.careers
Machine Learning Engineer, Marketplace is a Machine Learning Engineer role (full-time). with Mercor. in SAN FRANCISCO, US. Compensation shown: $130K–$500K. Imported listing (source: jobs-radar.com). Apply on the employer's site (jobs-radar.com).
Imported listing (source: jobs-radar.com) · Apply on jobs-radar.com
SAN FRANCISCO · US
$130K–$500K
Full-time
SENIOR
The sections below reproduce the third-party job description for reference. AIEngineer.careers does not write or control this text.
Imported job description
Sourced from jobs-radar.com
Mercor is hiring a Machine Learning Engineer for their Marketplace team to build the models and decision systems powering their hiring engine. This includes ranking, matching, recommendation, and optimization systems operating at global scale. The role is on-site in San Francisco, NYC, or London.
Responsibilities
- Build ranking and matching systems for candidates and opportunities
- Develop models for recommendation, personalization, and marketplace optimization
- Create retrieval, scoring, and decision pipelines at global scale
- Implement feedback loops that learn from downstream hiring outcomes
- Design real-time and batch inference systems for product-critical workflows
- Optimize ranking toward long-term hiring outcomes under delayed labels
- Balance marketplace objectives like fill rate, quality, speed, and conversion
Requirements
- 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
Mercor
San Francisco · US · 4816+ employees
Mercor is a software development company that organizes human intelligence to power the AI economy. It partners with AI labs and enterprises to provide scalable, expert-guided model training, reinforcement learning from human feedback (RLHF), and data labeling.
