Reality Labs Research (Reality Labs Research) brings together a multidisciplinary and highly interdisciplinary team of researchers and engineers to create the future of dexterous robotic manipulation. We are seeking an experienced Robotics Operations Engineer to own and scale our real-robot operations infrastructure. You will be responsible for designing, building, validating, and supporting robotic manipulation stations used for data collection, policy deployment, and real-robot evaluation. You will work closely with research scientists, policy engineers, and embodiment teams — serving as the critical bridge between research ideas and physical robot results. This is a high-impact, high-autonomy role: you will define processes, build tools, and unblock researchers across the team.
Responsibilities
Next Generation Robotics Stations: Evaluate new robotic arms and hands and architect next-gen robot stations Robot Station Operations: Own the end-to-end lifecycle of real robot stations — build-up, calibration, validation, maintenance, and teardown. Ensure stations are reliable and ready for data collection and policy testing Data Collection & Ingestion: Operate and support teleoperation data collection campaigns. Build and maintain pipelines for ingesting, validating, and organizing collected data to feed ML training workflows Policy Deployment & Testing: Deploy trained control policies on real robotic hardware. Run evaluations, diagnose failures, and report results to research teams. Benchmark sim-to-real gaps Tooling & Infrastructure: Develop and maintain tools that improve robot operations reliability, performance, and ease of use — including Docker environments, build systems, deployment automation, and monitoring/logging systems System Integration: Integrate new hardware (sensors, hands, cameras) and software components (drivers, controllers, perception modules) into existing robot stations. Debug and resolve cross-system issues spanning RTOS, Linux, and Windows Process Definition & Documentation: Define and document playbooks, runbooks, and standard operating procedures for robot operations. Continuously improve processes to reduce toil and increase station uptime Cross-Functional Collaboration: Partner with policy researchers, embodiment engineers, teleop engineers, and exoskeleton teams to plan and execute experiments on real robots Station Scaling: Lead the bring-up and commissioning of new robot stations across sites. Develop scalable approaches to multi-station management
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience Bachelor's degree in Computer Science, Computer Engineering, Robotics, Electrical Engineering, or equivalent practical experience 5+ years of industry experience in robotics software engineering, robotic systems integration, or robotics operations Proficiency in Python, including experience writing production-quality scripts and automation tools, and experience with Linux systems administration Experience with ROS or ROS 2 in real-world robotic systems (not just simulation) Hands-on experience building up, debugging, and maintaining physical robot systems (manipulators, multi-camera setups, sensor suites) Experience with containerization (Docker) and deployment automation tools Demonstrated ability to work autonomously, define priorities, and drive projects to completion with minimal direction Experience communicating cross-functionally to translate between research goals and engineering execution Experience with motion capture systems, force/torque sensors, or tactile sensing hardware Experience scaling robot operations across multiple stations or sites, with a track record of reducing operational toil in research environments Experience with dexterous manipulation systems, multi-fingered hands, or humanoid robots Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) Experience with ML training pipelines or ML Ops workflows (e.g., PyTorch, data validation frameworks) Master's degree in Robotics, Computer Science, Electrical Engineering, or related field Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Proficiency in C++ in addition to Python Experience with real-time control systems, including RTOS and hardware time synchronization