About the job
Amazon’s Frontier AI & Robotics (FAR) team is seeking a Member of Technical Staff to drive foundational research and build intelligent robotic systems from the ground up. In this role, you will operate at the intersection of innovative AI research and real-world robotics - conducting original research, publishing, and deploying your innovations into production systems at Amazon scale. We’re looking for researchers who think from first principles, push the boundaries of what’s possible, and take full ownership of turning breakthrough ideas into working systems.
Responsibilities
· Drive independent research initiatives across the full robotics stack, including robot co-design, manipulation mechanisms, innovative actuation and motor control strategies, state estimation, low-level control, system identification, reinforcement learning, and sim-to-real transfer, as well as foundation models for perception and manipulation
· Lead full-stack robotics projects from conceptualization through hardware deployment, taking a system-level approach that integrates actuator dynamics, sensor feedback (force/torque, IMUs, encoders), and electromechanical constraints with algorithmic development
· Develop and optimize control algorithms and sensing pipelines for physical robotic hardware, including motor characterization, actuator performance tuning, and robust sensor integration in production environments
· Collaborate with hardware, mechanical, and electrical engineering teams to ensure seamless integration of learned models across the robotics stack—from embedded compute and communication buses to actuator-level control
· Contribute to the team's technical strategy and help shape our approach to next-generation hardware-aware robotics challenges, including hardware-in-the-loop validation and prototype-to-deployment transitions
Qualifications
Minimum
- PhD or equivalent research experience, or Master's degree and 4+ years of CS, CE, ML or related field experience
- Experience programming in Java, C++, Python or related language
- Experience with hardware design, low-level motor/actuator control, state estimation, system identification, or complex electromechanical systems
- Experience developing and implementing deep learning models for physical systems
- Publications or patents at top-tier peer-reviewed conferences or journals
Preferred
- History of impactful first-author publications at major or top-tier Robotics/ML/AI conferences
- Experience with sim2real transfer, actuator modeling, or multi-task learning on physical robotic platforms
- Track record of deploying ML models on robotic hardware in production environments, including actuators, force/torque sensors, IMUs, and encoders
- Extensive programming skills in Python and PyTorch/JAX; familiarity with communication protocols used in robotic hardware (CAN, EtherCAT, RS-485)
- Background in computer vision, motor control, robotics, or related fields