About the role
Mercor's mission is to organize human intelligence to power the AI economy. As a Machine Learning Engineer on the Frontier Data Products team, you will build the ML systems that score, validate, and improve complex work products where correctness is nuanced 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 — not an offline benchmarks role. You will operate across models, data, backend systems, and product surfaces, working closely with backend engineers on a stack of Python, Temporal, Postgres, AWS, and LiteLLM. The architecture is not set — early engineers will define how quality is measured, how models and humans interact, and how the system compounds over time.
What we're looking for
Proven 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 use prompting, fine-tuning, heuristics, retrieval, human review, or simpler product constraints. Solid engineering fundamentals across the full ML stack beyond just modeling. Familiarity with LLM applications, model-assisted workflows, evaluation frameworks, or human-in-the-loop ML is a strong plus. Ability to hold ambiguity without paralysis and make reasonable bets with incomplete information. Preference for simple, inspectable ML systems that improve quickly and fail in understandable ways over impressive but opaque architectures.