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The NPI Engineer will manage manufacturing processes, oversee prototype builds, and ensure accurate BOMS while leading cross-functional teams.

The Site Lead will manage robot deployment, oversee onsite operations, service and maintain robots, and lead the site team.

The Deployment Engineer supports the development and deployment of humanoid robots at customer sites, ensuring testing and performance goals are met, while also analyzing data and providing feedback to engineering teams.

The Data Infrastructure Engineer will design, build, and maintain data tools for robot data management, supporting AI researchers and ensuring optimal data solutions across storage and compute resources.

Design and build native Android applications for robot data annotation, dataset review, and experiment tracking. Create fast, reliable mobile experiences for real-time data capture, integrate AI models to automate annotation, and collaborate with AI researchers, robot operators, and web tooling teams to ensure consistent workflows.

Design and implement real-time localization, ego-motion tracking, and state-estimation systems by fusing multi-modal sensors. Build scalable offline pose-tracking pipelines, multi-sensor calibration tools, and evaluation/testing frameworks. Collaborate cross-functionally to deploy reliable, high-performance software for humanoid robotics in real-world environments.

Own and manage Figure's external data vendor portfolio for data quality: source, contract, onboard/offboard vendors, set/enforce SLAs and acceptance criteria, run audits/calibration and vendor scorecards, coordinate capacity and delivery with engineering, ML, and internal coordinators.

Manage end-to-end data collection projects for humanoid robot training: translate requirements into capture plans, coordinate with engineering and data quality, track schedules and dependencies, resolve infrastructure and hardware blockers, write and maintain SOPs, and manage direct reports where applicable.

Own end-to-end project lifecycle for data quality: define review guidelines and SOPs, run audits, manage analysts and vendors, translate ML requirements into operational guidance, and lead training, calibration, and performance reviews.

Manage end-to-end data collection projects for humanoid robot training: translate requirements into capture plans, coordinate with engineering and Data Quality, track delivery and dependencies, resolve infrastructure/hardware issues, write SOPs, contribute to task design, and manage direct reports when applicable.