Mercor

Machine Learning Engineer, Frontier Data Products

Mercor
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3 months ago
San Francisco, CA, USA or New York, NY, USASenior
H1B Sponsor

Base Salary

$130k - $500k/yr

Responsibilities

  • Build ML systems that score, validate, and improve complex work products with imperfect labels.
  • Design evaluation frameworks for ambiguous tasks with partial, delayed, or disputed ground truth.
  • Turn review, disagreement, correction, and adjudication into measurable model and system improvements.
  • Own production ML quality, regression detection, drift, latency, cost, and explainability.
  • Improve model behavior through prompting, fine-tuning, retrieval, active learning, heuristics, and error analysis.
  • Integrate inference into durable, long-running workflows with backend engineers while preserving debuggability and human oversight.

Requirements

  • Track record of shipping ML systems that improved a real product, workflow, or business metric.
  • Strong judgment in model quality, evaluation design, error analysis, and production failure modes.
  • Comfort working in ambiguous problem spaces with imperfect labels and evolving definitions of correctness.
  • Ability to choose appropriately among prompting, fine-tuning, heuristics, retrieval, human review, and simpler product constraints.
  • Solid engineering fundamentals across the full ML stack, beyond modeling alone.
  • Familiarity with LLM applications, model-assisted workflows, evaluation frameworks, or human-in-the-loop ML is preferred.

Benefits

  • In-person work five days per week from the San Francisco, New York City, or London office.
  • Bi-annual performance bonus structure and equity grant.
  • Relocation and housing support may be available.
  • Meal, laundry, and personal wellness reimbursement programs.
  • Free Equinox membership.
  • Health, dental, and vision insurance.
Mercor

About Mercor

201-500 employees

We find the best experts in every professional domain and put their knowledge to work training frontier models. Through APEX, we measure whether those models can actually perform economically valuable work. We're also bringing that expertise to enterprises: deploying custom AI agents, staffing teams with vetted domain experts, and helping organizations encode their own knowledge into AI systems.

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