Robotics Infrastructure Engineer

Job description

Job responsibilities

• Own robot-side software (Python): Maintain the on-robot codebase that orchestrates arms, cameras, sensors, and I/O. Debug production hardware/software failures and ship fixes fast
• Build and maintain infrastructure as code: Manage cloud infrastructure — identity and access management, CI/CD credentials, secrets, container registries, cluster autoscaling — using declarative configuration and…
• Drive build system and packaging migrations: Own the transition of robot software packaging to reproducible, hermetic build systems. Maintain machine images, dev environments, and deployment pipelines
• Build simulation and testing infrastructure: Develop end-to-end simulation systems that validate robot behavior without physical hardware — camera projection, kinematics, placement validation, fleet-wide calibration
• Develop and operate AI-powered engineering automation: Build autonomous agents that run nightly CI triage, security audits, infrastructure compliance checks, and code quality sweeps.
• Improve observability and health monitoring: Instrument robot software with metrics and structured telemetry. Build alerting that catches problems before humans notice them
• Work across the stack: Touch frontend, backend, protobuf definitions, deployment tooling, and cloud services as needed. No part of the system is someone else's problem

Job requirements

• 3+ years of Python in a systems context — not web/ML Python, but the kind where you deal with processes, hardware I/O, async, and real-time constraints
• Strong Linux systems knowledge: Memory management, device management, systemd, containers, networking, kernel tuning
• Infrastructure as code experience: Declarative infrastructure and configuration management tools. You've managed IAM, CI runners, secrets, and machine images programmatically
• Experience with real hardware: Robot arms, depth cameras, grippers, force/torque sensors, pneumatics, or similar
• CI/CD ownership: You've not just used CI — you've owned it. Runner infrastructure, flaky test triage, build caching, GPU-enabled pipelines
• Comfort with AI coding agents: You've used tools like Claude Code, Cursor, Copilot Workspace, or similar to do real engineering work — not just autocomplete, but directing agents through multi-step debugging,…
• Strong debugging instincts: You can go from a vague production symptom to root cause across hardware, OS, network, and application layers
• Bias toward shipping over perfecting: You fix, monitor, iterate. Your commit history has more fix: than feat: and you're proud of that