Robotics Engineer Jobs

About The Position

Requirements

• Master's degree in Computer Science, Robotics, Engineering, or a related field (or equivalent experience).
• 5+ years of professional software engineering experience.
• Fluency across the kinds of software a robotics stack is made of — policy training, simulation, on-robot deployment — and the judgment to work effectively in whichever language and toolchain each of those calls for.
• Hands-on experience building and maintaining CI/CD pipelines (GitHub Actions, GitLab CI, or similar), with proven Docker and Linux fundamentals.
• Practical experience using coding agents or LLM-based developer tools in real work, and a point of view on where they succeed and where they fall over.
• A bias toward making other engineers faster, and the follow-through to get a practice embraced rather than just proposed.
• Clear written communication — a large part of this role is documentation, templates, and persuading teams to change how they work.

Nice To Haves

• Experience building agent skills, tool integrations, or MCP servers, and evaluating agent performance systematically.
• Familiarity with NVIDIA robotics products such as Jetson, Isaac Sim/Lab, or Isaac ROS.
• Background in robotics, embedded systems, or another domain where software has to run on real hardware.
• Experience managing GPU-backed CI infrastructure or self-hosted runner fleets.

Responsibilities

• Design APIs, error surfaces, tooling, and documentation so that agents can build with our robotics stack as effectively as human engineers — across multiple products and neighboring teams.
• Create agent skills and automated workflows that teams adopt in their own repos, and support embedded agents on the robot (code-as-policy and related approaches) where latency, safety, and determinism constraints apply.
• Run and integrate skill evaluations so agentic workflows are measured, not just deployed, and turn those evaluations into automated checks that catch regressions before they land.
• Own CI/CD across the applications repos — spanning CMake, Bazel, and Python builds — including runners, Docker images, dataset provisioning, nightly builds, and release and docs pipelines.
• Package patterns into docs, templates, and starter kits, then drive adoption — running the conversations with teams and following through until a practice is live in more than one repo.

Benefits

• equity
• benefits