2026 Applied Science Internship - United States, Undergrad Student Science Recruiting, Frontier AI & Robotics

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

This internship focuses on developing novel algorithms and modeling techniques at the intersection of LLMs and generative AI for robotics, tackling research problems in robotic perception, manipulation, and control. The role involves collaboration with cross-functional teams and requires a strong background in machine learning, deep learning, and/or robotics, with a publication record at top conferences.

What you'd actually do

  1. Develop novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of LLMs and generative AI for robotics
  2. Tackle challenging, groundbreaking research problems on production-scale data, with a focus on robotic perception, manipulation, and control
  3. Collaborate with cross-functional teams to solve complex business problems, leveraging your expertise in areas such as deep learning, reinforcement learning, computer vision, and motion planning
  4. Demonstrate the ability to work independently, thrive in a fast-paced, ever-changing environment, and communicate effectively with diverse stakeholders

Skills

Required

  • Strong background in machine learning, deep learning, and/or robotics
  • Proficiency in Python
  • Experience with PyTorch or JAX
  • Excellent problem-solving skills
  • Attention to detail
  • Ability to work collaboratively in a team
  • Enrolled in a Bachelor's degree or above
  • Eligible for and available for full-time (40 hours per week) internship for the whole duration of the internship/co-op

Nice to have

  • Publication record at science conferences such as NeurIPS, CVPR, ICRA, RSS, CoRL, and ICLR
  • Experience in areas such as multimodal LLMs, world models, image/video tokenization, real2Sim/Sim2real transfer, bimanual manipulation, open-vocabulary panoptic scene understanding, scaling up multi-modal LLMs, and end-to-end vision-language-action models
  • Experience programming in Java, C++
  • Publications at top-tier peer-reviewed conferences or journals
  • Experience in designing experiments and statistical analysis of results

What the JD emphasized

  • publication record at science conferences such as NeurIPS, CVPR, ICRA, RSS, CoRL, and ICLR
  • multimodal LLMs
  • world models
  • image/video tokenization
  • real2Sim/Sim2real transfer
  • bimanual manipulation
  • open-vocabulary panoptic scene understanding
  • scaling up multi-modal LLMs
  • end-to-end vision-language-action models

Other signals

  • novel algorithms
  • state-of-the-art LLMs
  • generative AI for robotics
  • robotic perception, manipulation, and control
  • deep learning, reinforcement learning, computer vision, motion planning