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

About the role

The 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 cutting-edge AI research and real-world robotics - conducting original research, publishing, and deploying your innovations into production systems at Amazon scale.

Responsibilities

• Drive independent research initiatives across the robotics stack, including robot co-design, dexterous manipulation mechanisms, innovative actuation strategies, state estimation, low-level control, system identification, reinforcement learning, sim-to-real transfer, as well as foundation models focusing on breakthrough approaches in perception, and manipulation, for example open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, sim2real/real2sim techniques, end-to-end vision-language-action models, efficient model inference, video tokenization
• Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish
• Guide technical direction for full-stack robotics projects from conceptualization through deployment, taking a system-level approach that integrates hardware considerations with algorithmic development, ensuring robust performance in production environments
• Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications
• Contribute to team's technical decisions and influence implementation strategies to help shape our approach to next-generation robotics challenges
• Mentor fellow researchers while maintaining solid individual technical contributions

Qualifications

• PhD, or Master's degree and 4+ years of CS, CE, ML or related field experience
• Experience with any programming language such as Python, Java, C++
• Experience building machine learning models or developing algorithms for business application
• Experience in patents or publications at top-tier peer-reviewed conferences or journals
• Experience developing and implementing deep learning models and applying theoretical models in an applied environment