Senior Research Engineer, Humanoid Manipulation

Job description

Job responsibilities

• Advance Humanoid Manipulation Research and Development
• Design, implement, and evaluate manipulation strategies for humanoid robots across a wide range of tasks.
• Drive projects from early concepts through on-robot testing and deployment.
• Contribute to loco-manipulation capabilities that integrate perception, control, and planning.
• Build Systems That Bridge Research and Deployment
• Translate research ideas into reliable robotic systems that operate in real-world conditions.
• Ensure systems are robust, reproducible, and aligned with data collection and learning objectives.
• Develop experimental infrastructure to support rapid iteration and evaluation.
• Collaborate Across Disciplines
• Partner with mechanical and electrical engineers on hardware integration and system bring-up.
• Work closely with teleoperators and field teams to refine interfaces and improve manipulation performance.
• Act as a connective layer between autonomy research and applied robotics engineering.
• Rapidly Iterate and Deliver
• Prototype quickly, run experiments on hardware, and validate results in the field.
• Balance exploratory research with concrete deliverables that support near-term goals.
• Debug complex system-level issues spanning software, hardware, and learning.

Job requirements

• Bachelor’s, Master’s, or PhD in Robotics, Computer Science, Mechanical Engineering, or a related field.
• 4+ years of hands-on experience in robotic manipulation in academic or industry settings.
• Strong foundation in robot kinematics, dynamics, and control, with a focus on manipulation.
• Proven experience implementing and evaluating robotic systems on real hardware.
• Ability to work effectively in fast-paced, highly collaborative environments.
• Curiosity, ownership, and the confidence to challenge assumptions and propose new approaches.
• Experience working with humanoid robots or multi-fingered robotic hands.
• Background in learning-based manipulation, including imitation learning or reinforcement learning.
• Familiarity with teleoperation systems and human-in-the-loop data collection.
• Publications or open-source contributions in top robotics venues.
• Experience with large-scale robotics data collection and dataset management.
• Strong interest in bridging cutting-edge research with field-ready robotic systems.