Senior Software 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?

That signal is what this role owns. You'll build 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. And 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.

What you get to do in this role:

We're hiring across three areas. You'll anchor on one and touch the others; which one is a conversation we have with you, not a slot we drop you into.

Eval orchestration at scale

• The runtime that executes multi-turn agent scenarios end-to-end — stand up the environment and user simulator, drive the user↔agent↔world loop, collect transcripts, traces, and final state, run validators and scoring, tear down
• Scheduling, retries, high-concurrency execution, and run isolation at production dataset sizes
• Versioned specs, datasets, and reports, with run-to-run comparison as a first-class operation
• Consolidating evals that run today as one-off workflows onto a single orchestration service — one source of truth, one place to schedule and retry
• Establishing a reliability floor and an SLO for the harness itself
• Getting to self-serve, so any team runs an eval without bespoke integration

Agent observability and tracing

• Leading the move to OpenTelemetry-native observability for the agent platform, replacing the parallel per-service logging, correlation, and redaction mechanisms in use today
• The span data model for agent trajectories — prompts, tool calls, plan updates, outcomes — so a trajectory is queryable, not reconstructed by hand from log files
• Trace context propagation across async boundaries and sessions that stay alive for minutes or hours
• Making full prompts and completions survive the pipeline intact, and keeping eval traffic from contaminating its own data
• Fault attribution and cross-run diffing: which component actually broke, and what changed since the last green run