Reality Labs Research (RL-R) brings together a diverse and highly interdisciplinary team of researchers and engineers to create the future of dexterous robotic manipulation.
We are seeking a staff Research Engineer with deep expertise in software engineering, robotic systems integration, and machine learning. This role is highly interdisciplinary — you will own the end-to-end technical architecture spanning full-stack robotics development, deployment of learned control policies, and hands-on integration of robotic embodiments with human wearable sensing systems.
As a staff individual contributor, you will set the technical direction for complex multi-disciplinary systems, drive innovations that bridge research and production, and mentor engineers across teams. You'll collaborate closely with researchers, engineers, and designers, leveraging cutting-edge technology and advanced research facilities.
Responsibilities
- Architect & Own Real-Time Robotic Systems: Design and maintain real-time dexterous manipulation pipelines that integrate perception, planning, and control across multiple robotic platforms. Drive architectural decisions that enable rapid research iteration at scale
- Lead Data Capture & Retargeting Infrastructure: Architect motion capture integration, novel hardware prototypes, and human demonstration data collection systems. Build scalable processing pipelines for large multimodal datasets that enable efficient model training and real2sim transfer
- Drive ML-Systems Integration: Deploy and iterate on learned control policies (imitation learning, MPC, reinforcement learning) within full robotic systems. Partner with research teams to bridge the gap between algorithmic advances and real-world system performance
- Optimize Performance & System Reliability: Own runtime performance, debug complex system behaviors across the stack, and develop interactive demos and benchmarks that demonstrate research progress
- Set Technical Direction: Identify and drive cross-cutting technical improvements. Influence roadmap and priorities through deep system understanding and proactive problem identification
- Collaborate & Mentor Cross-Functionally: Work with diverse research and engineering teams to refine modules, drive end-to-end system improvements, and elevate the technical capabilities of the broader team
Minimum 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, or related technical field (or equivalent practical experience)
- 5+ years of experience in robotics engineering, including hands-on work with robotic platforms in industry or academic research settings
- Demonstrated experience with machine learning systems in a robotics context (e.g., learned control policies, perception models, or ML-driven planning)
- Full-stack systems engineering experience designing, building, and maintaining large-scale software-hardware systems
- Track record of driving complex, ambiguous technical projects from conception through delivery with minimal direction
- Experience communicating technical decisions and system designs to cross-functional stakeholders across research and engineering functions (e.g., design documents, technical reviews, project proposals) Experience building and operating systems that bridge research exploration and reliable deployment
- Background in computer vision, imitation learning, reinforcement learning, model-predictive control, or sim-to-real transfer
- Master's or Ph.D. 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
- Experience in dexterous manipulation, learned robotic policy deployment, or control theory applied to real hardware
- Experience designing data collection protocols and building high-quality ML datasets at scale
- History of mentoring engineers and influencing technical direction beyond your immediate team
- Deep familiarity with robotics frameworks (e.g., ROS/ROS2) and real-time robotic control systems
- Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)