Reinforcement Learning Research Intern – Autonomous Safety Trajectory

Reinforcement Learning Planning Research Intern | PlusAI

This is an internship at PlusAI, located in Santa Clara, CA. PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. This role is crucial for developing a Safety-Critical Trajectory Correction (STC) module, which will act as a real-time safety overlay, ensuring absolute safety by minimally perturbing intended trajectories when collision risks are detected.

The module will provide a continuous safety barrier for the autonomous vehicle fleet.

• Role:
  Internship
• Type:
  Temporary (Summer)
• Location:
  
  In-person, Santa Clara, CA
• Pay: $19–$65 hourly
• Mission:
  Design, train, and validate a Safety-Critical Trajectory Correction (STC) module using Deep Reinforcement Learning to provide a continuous, constrained safety barrier.
• Tech Stack:
  PyTorch, Tensorflow, Jax

• Research:
  Conduct groundbreaking research focused on reinforcement learning to generate safe trajectories for autonomous driving, with the potential to lead to publishable results.
• Development:
  Develop and benchmark cutting-edge deep learning techniques.
• Collaboration:
  
  Collaborate with team members to optimize and seamlessly integrate developed techniques into the production perception/AV stack.
• Ownership:
  Own the development of a Safety-Critical Trajectory Correction (STC) module.
• Design & Validate:
  Design, train, and validate the STC architecture using Deep Reinforcement Learning.

• Background:
  Student pursuing an MS or PhD in Computer Science, Electrical Engineering, mathematics, statistics, or a related field.
• Experience:
  
  1-2 years experience implementing and training models in at least one deep learning framework.
• Skills:
  
  Thorough understanding of reinforcement learning.
• Bonus:
  Past experience designing, implementing, and training deep reinforcement learning models;
  Past experience in projects related to autonomous driving.