Sr. Machine Learning Engineer, Anonymization

The role

You will build ML systems that score, validate, and improve complex work products. You will design evaluation frameworks and feedback loops to improve model and system performance.

What you will do

• Build ML systems that score, validate, and improve complex work products
• Design evaluation frameworks for ambiguous tasks
• Build feedback loops that turn review, disagreement, correction, and adjudication into measurable improvements
• Own production ML behavior end-to-end including precision/recall, regression, drift, latency, cost, and explainability
• Improve model quality using prompting, fine-tuning, retrieval, active learning, heuristics, and error analysis
• Partner with backend engineers to integrate inference into durable, long-running workflows
• Move fast on a high-ownership codebase
• Operate across models, data, backend systems, and product surfaces
• Debug production ML failures in live, long-running workflows

Required qualifications

• Track record of shipping ML systems that improved 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
• Sound judgment about when to reach for prompting, fine-tuning, heuristics, retrieval, human review, or a simpler product constraint
• Solid engineering fundamentals across the full ML stack
• 4+ years of experience

Nice to have

• Familiarity with LLM applications, model-assisted workflows, evaluation frameworks, or human-in-the-loop ML

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

About the company

Mercor builds infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. The company is profitable and recently raised a $750 million Series D at a $20 billion valuation.