Research Engineer - Reinforcement Learning, Self-Driving

Applied Intuition•Sunnyvale, CA

1d•Onsite

About The Position

Applied Intuition is seeking multiple passionate Research Engineers to join their Research Group. This group focuses on creating cutting-edge technology for next-generation physical AI, specifically in end-to-end autonomous driving and robotic generalist applications. Researchers will be supported by industry-leading tools and infrastructure, including access to millions of miles of data and the ability to deploy developed methods into various autonomous and robotic systems. The role involves contributing to research publications, collaborating with engineering teams on ADAS, data, and simulation for mass production vehicles, and developing neural simulation/generation tools for autonomy development. The company culture is fast-paced and customer-focused, with a strong emphasis on autonomy and robotics best practices. Applied Intuition is an in-office company, with the expectation of working from the office five days a week, though occasional flexibility is recognized.

Requirements

Hands-on experience in at least one of the following fields: Self-play RL and imitation learning, behavior learning
Hands-on experience in VLA post-training for autonomy or robotics
Hands-on experience in large-scale closed-loop RL in driving simulation
Hands-on experience in large-scale RL training infrastructure (Ray preferred)
Passion for next-generation, scalable autonomy and robotics for real-world systems
Strong engineering and research skills
Ability to work both independently and collaboratively on projects
Technical experience in: Python, Pytorch, computer vision, robotics systems, and distributed machine learning model training

Nice To Haves

Industry experience on relevant topics (self-driving application preferred)
MSc or PhD in machine learning and computer vision with autonomy and robotics applications or closely related field
Passion for building and shipping customer-focused software frameworks or tools

Responsibilities

Conduct research on reinforcement learning (RL) and its training infrastructure, including topics on large-scale self-play RL, VLA post-training, large-scale closed-loop RL based on neural simulation with applications to autonomous driving
Work closely with Research Scientists and interns on high-quality research publications to submit to top-tier conferences
Collaborate with engineering teams on ADAS, data, and simulation to deploy end-to-end algorithms for mass production vehicles, and neural simulation/generation for tools supporting autonomy development