Robotics Systems & Data Operations Specialist — Contractor

The role is ON-SITE, full-time and contractor-based. Minimum duration is 3-6 months, renewable. About us We're a stealth startup at the frontier of physical AI, building foundation models for robotics and training the world models that will power the next generation of thinking machines. Founded by veterans of the industrial AI frontier, we’re a collective of elite researchers, engineers, and operational experts dedicated to advancing AI and robotic learning. Early-stage. Fast-paced. Hands-on. If you're a builder ready to make real impact from day one, you've found the right place. Role Description We're looking for a System Engineering and Operation Specialist who thrives at the intersection of hardware and software. You'll own the entire system — running experiments, driving robot operations, and turning raw data into training fuel that accelerates our models. Able to contribute to cutting edge research. If you move fast, take initiative, and communicate proactively when it counts, you'll fit right in. Responsibilities Design and operate end-to-end teleoperation systems with strict latency and motion-quality requirements, generating high-fidelity demonstration data for foundation model training. Collect, annotate, and curate multimodal robot learning datasets — including teleoperation demonstrations, reinforcement learning rollouts, and sensorized human motion capture — ensuring data quality and consistency across collection sessions. Instrument and maintain data collection pipelines for humanoid, manipulator, and mobile manipulation platforms, capturing proprioceptive, exteroceptive, and human motion signals at the fidelity required for generalist policy training. Develop and iterate on data validation procedures and failure analysis workflows to identify and eliminate noise, artifacts, and distribution gaps in training datasets. Bring up new robot embodiments — including humanoids, robotic arms, and mobile manipulation platforms — with a deep understanding of how hardware variation impacts learned policy generalization. Document procedures, create runbooks, and maintain rigorous change-management and incident-management practices across robot operations and data infrastructure. Communicate proactively with research and ML teams on data collection status, system incidents, and experiment outcomes — translating operational findings into actionable training insights. Support on-site operations in San Francisco, CA, including occasional off-hours availability during critical release windows or high-priority data collection sprints. Qualifications BS or MS in Electrical Engineering, Mechanical Engineering, Software Engineering, Computer Science, Robotics, or Mechatronics — or equivalent hands‑on experience building and operating real robot systems. Hands‑on experience with robotics-related systems, including latency-sensitive control architectures and motion-quality optimization across one or more robot embodiments (robots, manipulators, or mobile platforms). Experience designing or operating multimodal data collection pipelines, including teleoperation demonstrations, runtime rollout logging plus evaluation, and/or sensorized human motion capture systems. Strong analytical skills for interpreting system metrics, sensor logs, and performance data — with the ability to identify dataset artifacts, distribution gaps, and failure modes that affect downstream model training. Proven troubleshooting ability across hardware, software, and network layers in a production or operational robotics environment. Hands‑on experience administering Linux-based systems (e.g. Ubuntu) in operational or lab settings. Strong documentation discipline — capable of producing runbooks, validation procedures, and incident reports that meet engineering and research team standards. Familiarity with robot learning paradigms — particularly imitation learning, behavior cloning, or foundation model training workflows — and a clear understanding of how data quality and diversity impact policy generalization. ML and robot learning mindset — familiarity with how data feeds into model training pipelines and a curiosity for improving learning outcomes through better data. Experience with data collection systems for robots and sensorized humans — including wearable sensors, motion capture, or instrumented hardware used to generate high-quality training datasets. #J-18808-Ljbffr Stealth Startup