Member of Technical Staff - Hardware Science, Frontier AI & Robotics (far)

Amazon Amazon · Big Tech · San Francisco, CA · Applied Science

This role focuses on building and deploying intelligent robotic systems by developing foundation models for perception and manipulation, integrating them with hardware, and driving research from conceptualization to production at Amazon scale. It involves deep learning for physical systems, control algorithms, and collaboration with hardware engineering teams.

What you'd actually do

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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

Skills

Required

  • 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

Nice to have

  • History of impactful first-author publications at ma

What the JD emphasized

  • publications or patents at top-tier peer-reviewed conferences or journals
  • History of impactful first-author publications at ma

Other signals

  • building intelligent robotic systems
  • deploying innovations into production systems at Amazon scale
  • foundation models that enable robots to perceive, understand, and interact with the physical world
  • hardware-aware robotics challenges
  • prototype-to-deployment transitions