Current Openings

The role

This is a founding-cohort role for someone who wants to go deep. You'll pilot robots across a growing range of tasks — inspection, grading, and authentication; picking, packing, and sorting; returns processing; and kitting/light-assembly work — and help define what "great teleoperation" looks like as the role and tooling are still being built.

What you'll do

• Pilot robots across a range of evolving, sometimes unscripted tasks
• Detect, troubleshoot, and recover from errors live, without a script to follow
• Collect and annotate data from piloting sessions to support model training
• Evaluate ML model performance and report back findings to the engineering team
• Contribute to a shared library of SOPs — turning what you learn piloting each new task into documentation the next operator (and robot) builds on
• Help us think through better ways for the robot to perform tasks and structure workflows
• Work with Claude Code and GitHub as part of the team's day-to-day tooling

What we're looking for

• Full use of both hands/arms; excellent hand-eye coordination and manual dexterity
• A track record of reliable, high-quality output in a role where consistency matters — not just occasional strong performance
• Comfort giving structured, specific feedback on system or robot behavior — you'll be a direct feedback loop into how the tech gets better, not just an end user of it
• Ability to operate with minimal supervision across evolving, sometimes unscripted tasks, and to exercise good judgment about when to flag something up
• Generally tech-savvy — comfortable picking up new tools and software quickly (you'll work with things like GitHub and AI coding assistants such as Claude Code); no coding background required, just someone who isn't thrown off by unfamiliar software
• Reliable on-site/hybrid availability, with some flexibility for occasional travel to support new deployments
• No robotics background required — we've hired people from competitive gaming, esports, and other high-precision skill backgrounds

Your role

• Design and develop robust robotics software components: motion, planning, navigation, and execution on real robots, not just in simulation.
• Build pipelines that hold under real terrain constraints: latency, sensor noise, dynamic obstacles, and operational uncertainty.
• Implement fault recovery logic and diagnostic tooling that make the difference between a demo and a deployment.
• Work end-to-end: prototype, validate in simulation, and stabilize on physical hardware.
• Contribute to software architecture and code quality on a team that moves fast and builds to last.

What you bring

• 5-10 years in robotics software development, with a focus on planning, control, and/or navigation.
• Strong Python required. C++ is a plus.
• ROS/ROS2 experience and integration on complex physical systems.
• Solid grasp of trajectory planning algorithms, navigation, and motion control fundamentals.
• Familiarity with simulation environments like Gazebo, Unity, or equivalent.
• Bonus: safety-critical systems, real-time constraints, numerical optimization, and advanced calibration.

Your role

• Design and build AI and data pipelines for robotics: data ingestion, storage, transformation, labeling, training, evaluation, and deployment.
• Work with multimodal robot data: images, video, trajectories, actions, task sequences, operator inputs, and execution outcomes.
• Contribute to learning systems that improve manipulation reliability in complex, variable, real-world environments.
• Develop and fine-tune models for perception, action, task understanding, and robot behavior.
• Build evaluation loops that connect model performance to real robot execution, not just offline benchmarks.
• Work closely with robotics software engineers to deploy, test, debug, and improve AI components on physical systems.

What you bring

• 5+ years in applied AI, machine learning, robotics, computer vision, or closely related fields.
• Strong Python required. PyTorch experience expected.
• Experience building production-grade data or ML pipelines, from raw data to deployed models.
• Solid understanding of deep learning, computer vision, and multimodal models.
• Familiarity with supervised, self-supervised, reinforcement learning, imitation learning, or learning from demonstration.
• Practical experience working in collaborative software teams, using tools such as Docker, Git, CI/CD, and cloud infrastructure.
• Bonus: robotics experience, VLM/VLA models, manipulation, teleoperation data, simulation, Kubernetes, or deployment on edge/robotic systems.