About the Role
We're assisting a well-funded robotics startup in the industrial automation space with their search for Robotics Infrastructure Engineers. Their platform allows clients to utilize robotics as a service , increasing efficiency while keeping costs manageable. The role will work onsite in their Watertown, MA office 5 days a week. What you'll do: 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 What you'll bring: 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