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 reproducible builds
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. Design the interfaces and instructions that make agents effective at real engineering work
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
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, refactoring, and infrastructure tasks. You understand their failure modes and know when to trust vs. verify
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
Nice to Have
NixOS or reproducible build system experience
Experience building or operating autonomous engineering agents/bots
Robotics simulation (kinematics, camera models, physics)
gRPC / Protocol Buffers
Managed network infrastructure, VPNs, overlay networks
Time-series databases and observability stacks
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