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 The debug surface support and harness engineers use, and the tracing contract with the team that builds the agent Stateful simulation 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.
Qualifications To be successful in this role you have: Experience in at least 3 of these: 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 Required: 0-2 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 For positions in this location, we offer a base pay of $113,500-175,900, plus equity (when applicable), variable/incentive compensation and benefits. Sales positions generally offer a competitive On Target Earnings (OTE) incentive compensation structure.
Please note that the base pay shown is a guideline, and individual total compensation will vary based on factors such as qualifications, skill level, competencies and work location. We also offer health plans, including flexible spending accounts, a 401(k) Plan with company match, ESPP, matching donations, a flexible time away plan and family leave programs. Compensation is based on the geographic location in which the role is located and is subject to change based on work location.
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