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

Amazon Amazon · Big Tech · San Francisco, CA · Machine Learning Science

Research role focused on developing foundation models for robotics, involving perception, manipulation, and multi-modal learning, with a goal of real-world deployment.

What you'd actually do

  1. Drive independent research initiatives across the robotics stack, including robotics 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
  2. Design and implement novel deep learning architectures that push the boundaries of what robots can understand and accomplish
  3. Lead 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
  4. Collaborate with platform and hardware teams to ensure seamless integration across the entire robotics stack, optimizing and scaling models for real-world applications
  5. Contribute to the team's technical strategy and help shape our approach to next-generation robotics challenges

Skills

Required

  • PhD, 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 in patents or publications at top-tier peer-reviewed conferences or journals
  • Experience applying theoretical models in an applied environment
  • Experience building machine learning models or developing algorithms for business application
  • Experience developing and implementing deep learning models
  • Track record of solving complex technical problems

Nice to have

  • Experience using Unix/Linux
  • Experience in professional software development
  • First-author publications at major or top-tier ML/Robotics/AI conferences
  • Experience with foundation models or large language models
  • Background in Robotics, computer vision, or related fields
  • Experience with sim2real transfer or multi-task learning
  • Familiarity with distributed training systems
  • Track record of deploying ML models in production environments
  • Experience with large-scale robotic systems
  • Extensive programming skills in Python and PyTorch/JAX

What the JD emphasized

  • publication track record
  • first-author publications
  • deploy ML models in production environments

Other signals

  • foundation models
  • robotics
  • multi-modal
  • research
  • deployment