Tech Lead, Agent Eval Platform

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.

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

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

The debug surface support and harness engineers use, and the tracing contract with the team that builds the agent

The simulation environment itself: stateful fakes of the enterprise systems agents call — ITSM, HR, knowledge bases, inventory — backed by a real datastore that persists changes during a run, so a created ticket is visible to a later read

Per-run data injection and programmatic setup/teardown so every run is hermetic and repeatable

LLM-driven user simulators for open-ended personas, and scripted state-machine simulators for deterministic flows

Contract-testing mocks against real API schemas in CI, so simulation fidelity can't quietly drift as vendor APIs change

Ahead of us: isolated sandbox environments reproducing the config, identity, search content, and permissions an agent actually reads — provisioned from an identical baseline and torn down every run

And across all three: laying the foundation for using eval signal to optimize the agent, not just measure it.

Distributed systems: idempotency, delivery guarantees, isolation, and — unusually central here — determinism and reproducibility

Orchestration and workflow runtimes: DAG execution, scheduling, retries, backfills, high-concurrency job systems (Temporal, Airflow, Argo, or something you built yourself)

Observability internals as a builder , not just a user: OpenTelemetry SDKs and collectors, semantic conventions, span context propagation, high-cardinality trace data

Concurrent and async programming: Python asyncio, Go concurrency, structured cancellation

Data-intensive pipelines: high-volume ingest, schema evolution, sampling and retention trade-offs

gRPC/protobuf service and interface design

Ability to tech lead other engineers and the end to end delivery of a project. Good communication and soft skills.

10+ years building production backend or infrastructure systems

Strong in Python or Go (ideally both)

Experience designing and operating systems that handle real traffic at scale

Comfort making a non-deterministic system measurable. You don't need an ML background — but you should find it interesting to turn fuzzy agent behavior into a signal engineers are willing to gate releases on

Comfort with ambiguity; these are novel problems without textbook solutions

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