About The Position

You will be the engineer who keeps the autonomy stack healthy, fast, and trustworthy. This role spans code maintenance and platform quality, the validation and simulation pipeline that gates every release, and the triage-and-fix loop that turns reported road cases into resolved issues. If you take pride in a codebase that stays clean as it scales and a validation system that catches regressions before they reach the road, this is your role. The work is less about any single algorithm and more about the engineering backbone that lets the whole team move quickly without breaking things.

Requirements

  • B.S. in Computer Science or a related field, or equivalent practical experience.
  • Strong C++ and Python, with sound instincts for testing, code review, and keeping a large codebase healthy.
  • Experience building or operating CI/validation/test infrastructure for a complex software system.
  • Strong debugging skills — comfortable reproducing and root-causing issues across an unfamiliar, multi-component stack.

Nice To Haves

  • Experience with simulation, regression frameworks, or validation pipelines for robotics or autonomous systems.
  • Experience with data and logging infrastructure (large-scale log replay, observability, debugging tooling).
  • Familiarity with autonomy, robotics, or another real-time, safety-critical domain.
  • Experience defining metrics and quality gates that engineering and product teams rely on for release decisions.

Responsibilities

  • Own and improve core autonomy code: refactoring, interface hygiene, build and runtime performance, and day-to-day maintenance to keep a fast-growing codebase maintainable.
  • Build and operate the validation and regression pipeline, including scenario suites, metric definitions, sim-vs-road consistency checks, and release-gating criteria.
  • Improve simulation infrastructure: determinism, version pinning, scenario tooling, and diffing that makes the cause of a regression obvious.
  • Run the triage loop: take cases reported from the road and from simulation, reproduce them, localize the root cause, and either fix them or route them to the right owner with a clear, actionable writeup.
  • Build the debugging and observability tooling (logging, replay, per-frame snapshotting) that makes failures fast to diagnose.
  • Partner with the algorithm engineers, the simulation team, and operations to keep the path from "case reported" to "fix verified" short and reliable.
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