# Sr. Machine Learning Engineer, Anonymization Source: https://realanalystjobs.com/job/sr-machine-learning-engineer-anonymization-mercor-16d44e35 Sr. Machine Learning Engineer, Anonymization at Mercor, an on-site role based in San Francisco, California, USA. Experience level: 4+ Years. Pay stated in the posting: $180K to $500K. Posted on 29 May 2026, read from Mercor's own careers page (Ashby), which RealAnalystJobs re-checks daily and removes the role when it closes. Updated 15 November 2026. ## Key facts - Company: [Mercor](https://realanalystjobs.com/companies/mercor) - Location: San Francisco, California, USA - Work mode: On-site - Pay stated in the posting: $180K to $500K - Posted: May 29, 2026 - Employment type: Full time - Experience level: 4+ Years - Source: Mercor's careers page (Ashby), re-checked daily Skills: [Python](https://realanalystjobs.com/skills/python), AWS ## About this role About Mercor Mercor’s mission is to organize human intelligence for the AI economy. We build the infrastructure that enables frontier AI labs and enterprises to train, evaluate, and improve AI on real-world professional work. More than five million experts have joined our network, and we pay out over $5 million daily for their knowledge and expertise. We built our training and evaluation infrastructure alongside the world’s leading AI labs. Now, we’re bringing those capabilities to enterprises across industries, enabling them to train, evaluate, and improve AI using their own expertise and standards. Our APEX benchmarks measure how AI performs on economically valuable work, while our enterprise evaluations and post-training capabilities turn those insights into measurable improvements across real-world workflows. Our work sits at the intersection of frontier AI, human expertise, and real-world application. We’re solving problems few companies have encountered, with the opportunity to influence what the world’s most advanced AI systems become capable of doing. We’re looking for people who bring different perspectives, take ownership, exercise sound judgment, and hold themselves to an exceptionally high standard. You’ll have the opportunity to do consequential work alongside talented colleagues who challenge one another, collaborate openly, and share a commitment to excellence. Mercor is profitable and recently raised a $750 million Series D at a $20 billion valuation. We work in person five days a week in San Francisco, New York, and London. About the Role: Frontier AI companies are increasingly bottlenecked on expert judgment - capturing it reliably, validating it at scale, and turning it into durable model behavior. This role sits at the center of that problem. You'll build the ML systems that power Mercor's Frontier Data Products: the infrastructure that scores, validates, and improves complex work products where correctness is rarely binary and labels are often noisy, delayed, or disputed. A single job can stay live for days, interleaving model inference, automated checks, expert review, disagreement resolution, and feedback loops. Your work determines how models reason over ambiguous inputs, when they should defer to humans, how quality is measured, and how feedback compounds into better systems over time. This is applied ML product engineering under real production constraints - incomplete ground truth, shifting requirements, latency and cost tradeoffs, and workflows where a silent model failure corrupts the final output. It is not an offline benchmarks role. What You'll Do • 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. What Makes This Role Different • The architecture is not set - early engineers will define how quality is measured, how models and humans interact, where automation is trusted, and how the system compounds over time. • The feedback loop is short: shipping a model behavior change directly and visibly affects what customers receive. • You're working on a strategically central product area at Mercor at a moment when frontier AI companies have no good solution to the problem you're solving. Day-to-Day • Moving fast on a young, high-ownership codebase where your decisions have long-term architectural weight. • Operating across models, data, backend systems, and product surfaces - context switching is the default, not the exception. • Debugging production ML failures in live, long-running workflows where silent errors matter. • Working closely with backend engineers on a stack of Python, Temporal, Postgres, AWS, and LiteLLM. • Balancing automation confidence with human review - knowing when to defer is as important as knowing when to ship. What We're Looking For • 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 where labels are imperfect and correctness evolves. • 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 - not just modeling. • Familiarity with LLM applications, model-assisted workflows, evaluation frameworks, or human-in-the-loop ML is a strong plus. You're likely someone who: • Has 4+ years of experience • Defaults to simple, inspectable ML systems that improve quickly and fail in understandable ways - not the most impressive architecture. • Gets uncomfortable when a model ships without a clear evaluation story. • Can hold ambiguity without paralysis and make reasonable bets with incomplete information. • Cares about the real-world output of the system, not just the benchmark. Benefits: • Generous equity grant vested over 4 years • Health, dental, and vision insurance • 401(k) with company match • Unlimited paid vacation and sick days • Parental leave for birthing and non-birthing parents • Free mental health services • $200 monthly wellness reimbursement • $1.5K monthly meal stipend • $200 monthly laundry reimbursement • Free Equinox membership • Up to Description from Mercor's public careers feed, reproduced so you can read the role here. Apply on the company's own site; Real Analyst Jobs never submits anything for you. ## How to apply Open [this role on Real Analyst Jobs](https://realanalystjobs.com/job/sr-machine-learning-engineer-anonymization-mercor-16d44e35) and use Apply now there to reach the employer's application. The listing is re-checked against Mercor's careers page daily and removed when it closes. ## Mercor Analyst roles on Real Analyst Jobs. [More roles at Mercor](https://realanalystjobs.com/companies/mercor) ## More like this - [Data analyst jobs in the USA](https://realanalystjobs.com/jobs/data-analyst-jobs-in-usa) - [Analyst job market report](https://realanalystjobs.com/analyst-job-market) - [What analyst roles pay](https://realanalystjobs.com/blog/data-analyst-salaries) ## Sources and links - [This role on Real Analyst Jobs](https://realanalystjobs.com/job/sr-machine-learning-engineer-anonymization-mercor-16d44e35) - [Mercor on Real Analyst Jobs](https://realanalystjobs.com/companies/mercor) - [All analyst jobs](https://realanalystjobs.com/jobs)