Research Engineer Robotics (Systems)

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

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)