Principal Applied Scientist, Far (frontier AI & Robotics)

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

Lead the development of breakthrough foundation models for robotics, focusing on perception, manipulation, and interaction with the world. This role involves hands-on research, algorithm design, and scaling models for real-world deployment at Amazon scale, with a focus on multi-modal and efficient architectures.

What you'd actually do

  1. Lead technical initiatives in robotics foundation models, driving breakthrough approaches through hands-on research and development in areas like 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. Guide technical direction for specific research initiatives, ensuring robust performance in production environments
  4. Mentor and support fellow scientists while maintaining strong individual technical contributions
  5. Collaborate with engineering teams to optimize and scale models for real-world applications

Skills

Required

  • PhD, or Master's degree and 6+ years of applied research experience
  • 5+ years of industry or academic research experience
  • Experience programming in Java, C++, Python or related language
  • 5+ years of building machine learning models or developing algorithms for business application experience
  • Experience in patents or publication at top-tier conferences (NeurIPS, ICML, ICLR, CVPR, ICCV, RSS, CoRL)
  • Extensive track record of leading technical projects
  • Experience mentoring junior scientists / engineers
  • Demonstrated expertise in deep learning and model development

Nice to have

  • Experience as first-author publications at major conferences
  • Experience leading research initiatives in robotics or foundation models
  • Track record of successful production ML deployments
  • Experience with large-scale distributed systems
  • Experience in technical leadership and team mentorship
  • Experience bridging research with practical engineering implementation

What the JD emphasized

  • publication track record
  • leading technical projects
  • mentoring junior scientists / engineers

Other signals

  • foundation models
  • robotics
  • multi-modal
  • large-scale