Mountain View, CA (On-Site) · Full-time Mountain View, CA (On-Site)

Robotics Engineer/Researcher - Data Engine & Deployment - Teleoperation Systems, Data Pipelines, On-Robot Systems Join our team to build the data engine behind general-purpose robot policies. You'll own the pipeline from teleoperated demonstration collection through curation and quality control to on-robot deployment — the systems that turn robot time into training data, and trained policies into robots that work in the real world. We care more about your ability to build reliable, high-throughput systems around real robots than about any particular robot, task, or sensor you've used before. Requirements01 BS, MS, or PhD in Robotics, Computer Science, Electrical Engineering, or related field — or equivalent experience02 Strong software engineering skills in Python and C++ in Linux environments: you write robust, maintainable systems code that runs on real hardware03 Hands-on robotics experience: hardware bring-up, sensor and actuator integration, calibration, and debugging full-stack issues on physical systems04 Experience building teleoperation and demonstration-collection systems — rigs, operator interfaces, and workflows — and scaling collection throughput and operator efficiency05 Experience building data pipelines for multimodal robot data: ingestion, time synchronization across sensors, storage and dataset formats, curation, filtering, and annotation tooling06 Experience deploying learned policies on real robots: real-time inference, latency and throughput optimization, safety monitors, and graceful failure handling07 Comfortable working with multimodal sensor streams (RGB/depth cameras, proprioception, tactile, force-torque) — drivers, logging, and synchronized capture08 Rigorous about data quality: metrics and visualization for dataset coverage and consistency, automated QA, and regression testing of the collection-to-deployment loop09 (+) Experience with ROS 2 or comparable robotics middleware, real-time systems, and containerized deployment across a fleet of robots10 (+) Familiarity with robot learning workflows (imitation learning, vision-language-action models) — enough to shape data collection around what models actually need11 (+) Experience with dexterous hands, tactile sensing, or contact-rich manipulation setups Details & responsibilities01 Design and build the teleoperation and demonstration-collection stack — rigs, operator interfaces, and workflows that maximize throughput and data quality02 Run data collection operations end to end: task and protocol design, operator onboarding, and day-to-day collection on real robots03 Build the pipeline from robot to training set: ingestion, time synchronization of multimodal sensor streams, storage and dataset formats, curation, filtering, and annotation tooling04 Develop the QA, metrics, and visualization tooling that keeps datasets consistent, well-covered, and trustworthy05 Deploy trained policies to real hardware: real-time inference, latency optimization, safety monitors, and graceful failure handling06 Bring up and integrate sensors and hardware — cameras, tactile, force-torque — across collection and deployment rigs07 Collaborate across AI, hardware, and perception teams to close the loop from deployment results back into data collectio01 Competitive salary and meaningful equity02 Full health, dental, and vision insurance03 Access to custom-built dexterous robots04 Collaboration with leading researchers in robotics and AI05 Backed by YC and top-tier investors06 High-ownership role with the opportunity to lead core initiatives in real-world robot learning