Tech Lead, Robot Management System

Company Description

Roboligent is an Austin-based robotics company building Robin — a force-controlled bimanual mobile manipulator for warehouse and industrial work. Our patent-pending actuators give robots human-like sensitivity, so they work alongside people instead of behind cages. Robin is deployed with real customers today, on real production floors.

The engineering team is small and the scope is focused. Work ships to real robots in real customer environments within days, not quarters.

Role Description

You will own the Robot Management System (RMS) — the software layer through which every Robin is commanded, coordinated, monitored, and improved.

RMS is not a fleet dashboard. It is closer to an operating system for a fleet of mobile manipulators, spanning orchestration, traffic and resource control, the data loop back into training, and fleet-wide monitoring and diagnostics.

You own the platform architecture, the orchestration and traffic core, and the operator-facing surface. The training pipeline is built by our AI team; you own the contract it plugs into, not the models themselves.

What you'll own

• Architecture of RMS end to end — the contracts between operator surface, fleet services, and the robot's onboard stack
• Multi-robot task orchestration — mission lifecycle, assignment, preemption, recovery from partial failure
• Shared-resource arbitration and traffic control across a growing fleet
• The operator experience — how a warehouse supervisor dispatches, monitors, and intervenes
• Fleet observability and diagnostics good enough to debug a customer site remotely

Qualifications

• 6+ years building and shipping production distributed systems, including at least one system you architected and owned end to end
• Demonstrated ownership of a platform or product surface, not just assigned features
• Clear technical writing — we will ask to see a design doc you wrote
• Real, hands-on experience directing AI coding agents on production work — defining interfaces and invariants precisely enough for an agent to execute against, and judging whether the result is actually correct
• Comfortable building verification harnesses that force failure, not just test suites that confirm the happy path
• Strongly preferred: robotics, AMR/AGV fleets, industrial automation, or comparable physical-systems experience
• TypeScript/React, Python, Kafka, MQTT/VDA5050, ROS 2 Humble — degrees useful signal, not required

Location and logistics

On-site in Austin, Texas. Not remote, not hybrid.

Must be authorized to work in the U.S. without sponsorship, now or in the future.

Process

About three weeks end to end, with feedback after every stage: an intro call with our CEO, two writing samples (a design doc and an agent configuration file), a technical screen with the software team, a paid 3-hour working session, final conversations, and references.