Senior Machine Learning Engineer, Agent Eval Platform

The Role

Moveworks' AI agents don't just generate text — they act. They plan, call tools, and change real state in enterprise systems on behalf of 5.5 million employees. That makes the central problem of our team an unusually hard measurement problem: how do you score what an agent did — across a multi-step trajectory through a world it changed — precisely enough that the score can teach it to do better?

This role owns building the judgement layer of our agent evaluation platform: the rubrics, the judges, the calibration against human labels, the methodology that makes a score mean something.

The payoff is larger than a report card — a judge good enough to grade a trajectory is a judge good enough to train against. The same calibrated signal that explains why an agent failed becomes the reward signal that stops it failing.

This isn't a pretraining role, and it isn't a testing role. It's applied ML at a point where the methodology genuinely isn't settled: LLMs judging LLMs is an open research problem, and we're working it against agents that take real, irreversible actions in stateful, multi-tenant enterprise environments.

Qualifications

• 5+ years in applied ML, data science, or ML-adjacent engineering, with a track record of work that shipped and got used
• Experience turning subjective human judgement into a measurement that holds up — one that other people, and ideally other models, can act on. This is the core of the job
• Strong applied ML fundamentals, and comfort treating LLMs as a component you evaluate, prompt, and fine-tune rather than one you pretrain
• Python, and the discipline to ship production-grade code rather than notebooks
• Ability to think and communicate clearly about complex problems — a large part of this job is convincing engineers that a number means what you say it means, and being right
• A high degree of ownership and a bias toward shipping at startup pace
• Comfort with ambiguity, and the judgement to know when a measurement is good enough to act on
• Experience in at least 3 of these areas: LLM-as-judge or automated evaluation design, and calibrating it against human judgement, Human annotation programs: rubric authoring, label quality, and annotator throughput as a real constraint, Search ranking, recsys, or online experimentation evaluation — golden-set staleness, offline/online divergence, side-by-side rater agreement. This is the closest existing analog to agentic eval, and it transfers directly, Fine-tuning and evaluating small models: SFT, preference tuning, distillation, Reward modeling, RLHF/RLAIF, or process reward models, Agent trajectory analysis and step-level fault attribution, Prompt engineering as an engineering discipline — versioned, tested, and measured, not tuned by vibes