Senior ML Engineer — Build Production Frontier AI Systems

Fdetimes; San Francisco (CA); On-site; USD 180,000 - 270,000; Full time

Benefits offered by this job

Bi-annual performance bonus

Generous equity grant

Relocation bonus up to $15k

Housing bonus $10K near office

Monthly meal stipend $1.5K

Equinox membership

Laundry reimbursement $200/mo

Personal wellness reimbursement $200/m

Health, Dental, Vision insurance

Job summary

Mercor is hiring for an ML Product Engineer to build and maintain the data products powering frontier AI. You will design evaluation frameworks and feedback loops, shaping how models reason over complex, noisy inputs and how automation interacts with human review.

You'll own end-to-end production ML behavior and collaborate with backend engineers to embed inference into durable, long-running workflows, balancing latency, cost, and explainability in a fast-paced, in-person setting.

Qualifications

• Experience shipping ML systems that improve a real product, workflow, or business metric.
• Strong instincts for model quality, evaluation design, error analysis, and production failure modes.
• Comfort operating in ambiguous problem spaces where labels are imperfect and correctness evolves.
• Sound judgment about when to reach for prompting, fine-tuning, retrieval, or a simpler product constraint.
• Solid engineering fundamentals across the full ML stack — not just modeling.
• Familiarity with LLM applications, model-assisted workflows, evaluation frameworks, or human-in-the-loop ML is a strong plus.

Responsibilities

• Build ML systems that score, validate, and improve complex work products where correctness is nuanced and labels are imperfect.
• Design evaluation frameworks for ambiguous tasks where ground truth is partial, delayed, or disputed.
• Build feedback loops that turn review, disagreement, correction, and adjudication into measurable model and system improvements.
• Own production ML behavior end-to-end: precision/recall tradeoffs, regression detection, drift, latency, cost, and explainability.
• Improve model quality using the right tool for the job — prompting, fine-tuning, retrieval, active learning, heuristics, and error analysis.
• Partner with backend engineers to integrate inference into durable, long-running workflows without sacrificing debuggability or human oversight.

Skills

ML systems shipping

Evaluation design

Ambiguous problem spaces

LLM applications

Production failure handling

End-to-end ML responsibility

Tools

Python

Temporal

Postgres

AWS

LiteLLM

Job description

Mercor is hiring for an ML Product Engineer to build and maintain the data products powering frontier AI. You will design evaluation frameworks and feedback loops, shaping how models reason over complex, noisy inputs and how automation interacts with human review.

You'll own end-to-end production ML behavior and collaborate with backend engineers to embed inference into durable, long-running workflows, balancing latency, cost, and explainability in a fast-paced, in-person setting.