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# Sr. Machine Learning Engineer, Anonymization

- **Company:** Mercor, Inc.
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Software Debugging, Python (Programming Language), PostgreSQL, Machine Learning, Large Language Models, Backend, LiteLLM, Machine Learning Operations, Human in the Loop
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25303_04ce783aca45e361e033c4170e75d2d8

## About the Role

* 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.

## Description

* 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.

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.