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Research Intern - Reinforcement Learning, Self-Driving

Research reinforcement-learning methods for autonomous driving and broader physical AI systems.

What the role actually is

This internship sits in Applied Intuition's research group and focuses on reinforcement learning for autonomy. The work includes closed-loop RL, self-play, VLA post-training, neural simulation, and methods that can transfer across self-driving and robotic systems.

What you would work on

  • Run research on reinforcement-learning topics for large-scale autonomy systems.
  • Explore self-play RL, neural-simulation-based closed-loop RL, reward learning, and behavior learning.
  • Collaborate with research scientists and engineers on publishable work.
  • Use Applied's data and infrastructure to connect research experiments to deployed autonomy programs.

What they are asking for

  • Current graduate study in machine learning, computer vision, graphics, robotics, or a related field.
  • A strong research background in reinforcement learning or adjacent autonomy topics.
  • Ability to work independently and collaboratively on research projects.
  • Interest in scalable autonomy and robotic systems.

Why this one is worth a look

Applied's research group works across autonomous vehicles, trucks, mining and construction machines, humanoids, and dexterous hands. That makes this a useful internship to watch if you care about RL methods that move beyond isolated demos into physical AI programs.

About Applied Intuition

Silicon Valley physical AI company building autonomy, simulation, and operating-system software for vehicles, drones, defense systems, and industrial machines.

Visit Applied Intuition

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