Robotics Infrastructure Engineer

Robotics Infrastructure Engineer

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