Robot Infrastructure Engineer (Collection Tooling)

• Applied Intuition is building a robot learning platform on Dana, its physical AI platform: the data infrastructure and training intelligence a company needs to make any robot learn an industrial task and keep improving it
• The robotics team builds that platform and uses it to deliver robot autonomy on customer lines, training, evaluating, and deploying policies on real robots doing real industrial tasks
• Everyone on the team works hands-on with hardware and sees their work reach customers
• As a Robot Infrastructure Engineer focused on collection tooling, you will make high-quality robot demonstrations and human corrections easy to capture and use
• You will build the teleoperation workflows, quality checks, and ingestion tools that turn a session at the robot into training data the team can trust
• This is a tooling role, not a role running a large collection operation
• We are open to candidates at different experience levels who meet the requirements
• We value demonstrated work on real systems, including equivalent practical experience, over a particular degree or title
• Build teleoperation and operator tools for leader arms, VR, and other interfaces, including calibration, action mapping, latency, and reliable recording
• Implement episode workflows for task instructions, start and stop, resets, annotations, success labels, and human takeover, keeping a clean boundary between policy actions and human corrections
• Build collection quality checks for frame drops, timestamp alignment, missing signals, calibration validity, and incomplete episodes, with feedback operators can act on immediately
• Build collection specifications and adapters that validate data delivered by external partners against task requirements, schemas, calibration, and provenance
• Keep raw recordings as the source of truth and make normalization and export to training formats reproducible
• Show collection progress and coverage against the task so the team collects the missing variations and hard cases, not more of the same
• Test everything on real robots and document setup and troubleshooting so engineers, operators, and partners can run collection without you in the room

Benefits

• Health Insurance: Applied covers 100% of the cost and offers medical, dental, vision, and life insurance
• Fitness Stipend: Our wellness program supports your health and fitness goals. We pay for your trainers, gym memberships, massages, and more!
• 401(k) Match: 50% employer matching to help you save money for retirement
• Learning Stipend: We invest in your learning and growth by reimbursing you for educational and training expenses
• Parental leave: 12-week, fully paid parental leave to bond with a newborn child or new adopted or foster child
• Catered lunch, expensed dinner, on-site meals and snacks- A Bachelor’s degree in Computer Science, Software Engineering, or equivalent
• Experience with synchronized video, robot state, actions, or other multimodal time-series data
• Working knowledge of coordinate frames, sensor calibration, data provenance, and how collection errors show up in trained models
• Strong Python skills and software engineering fundamentals, including APIs, tests, versioned schemas, and debugging data pipelines
• The ability to turn a task description into an executable collection workflow together with learning, hardware, and operations colleagues
• Experience building tools for other people, watching them fail in real use, and simplifying without hiding data-quality problems
• 1+ years of professional experience
• Built data collection, teleoperation, sensor recording, or human-in-the-loop tooling used on real robots or autonomous systems, and validated it on hardware
• Don’t meet every single requirement? If you’re excited about this role but your past experience doesn’t align perfectly with every qualification in the job description, we encourage you to apply anyway. You may be just the right candidate for this or other roles
• Experience with ROS 2, MCAP, LeRobot, HDF5, or adapters between robotics datasets
• Experience with VR teleoperation, leader-follower systems, motion retargeting, haptics, or takeover-based learning
• Experience with sensor fusion, offline trajectory reconstruction, coverage analysis, or active data collection
• Experience building video and trajectory inspection, annotation, dataset validation, or data grading tools