Who we are Moveworks: the Agentic AI Assistant platform that empowers the entire workforce. Our platform enables employees to converse with all of their business systems through natural language to quickly find answers and automate tasks. Backed by Greylock and Sequoia.
About 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? Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this 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.
We're hiring across three areas....
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