Research Engineer - Reinforcement Learning, Self-Driving

Applied Intuition•Sunnyvale, CA

1d•Onsite

About The Position

Applied Intuition, Inc. is powering the future of physical AI. Founded in 2017 and now valued at $15 billion, the Silicon Valley company is creating the digital infrastructure needed to bring intelligence to every moving machine on the planet. Applied Intuition services the automotive, defense, trucking, construction, mining and agriculture industries in three core areas: tools and infrastructure, operating systems, and autonomy. Eighteen of the top 20 global automakers, as well as the United States military and its allies, trust the company’s solutions to deliver physical intelligence. Applied Intuition is headquartered in Sunnyvale, California, with offices in Washington, D.C.; San Diego; Ft. Walton Beach, Florida; Ann Arbor, Michigan; London; Stuttgart; Munich; Stockholm; Bangalore; Seoul; and Tokyo. Learn more at applied.co. We are an in-office company, and our expectation is that employees primarily work from their Applied Intuition office 5 days a week. However, we also recognize the importance of flexibility and trust our employees to manage their schedules responsibly. This may include occasional remote work, starting the day with morning meetings from home before heading to the office, or leaving earlier when needed to accommodate family commitments. We are looking for multiple passionate Research Engineers to join the Research Group at Applied Intuition. The mission of the group is to create cutting-edge technology enabling next-generation physical AI, with emphasis on the two most challenging applications reshaping our everyday life: end-to-end autonomous driving and robotic generalist. We have a group composed of leading experts from top institutions and companies, recognized for their exceptional academic and industry contributions—including eight Best Paper awards at premier conferences and journals such as CVPR and ICRA. Learn more at appliedintuition.com/research. Supported by industry-leading tools and infra, researchers can access millions of miles of data from large fleets, and deploy methods they develop into various autonomous and robotic systems including self-driving cars/trucks, autonomous mining/construction machines, humanoid robots and dexterous hands. In addition to your research contributions, you will contribute to and learn from best practices in the autonomy and robotics industries within our fast-paced and customer-focused culture. Improvements deployed to our system immediately help our customers with their programs and deliver value to our business. We are open to all years of experience as long as the necessary requirements are met, including those with potential Tech Lead and Manager capacity. At Applied Intuition, you will: 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 our 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

Requirements

Hands-on experience in at least one of the following fields
Self-play RL and imitation learning, behavior learning
VLA post-training for autonomy or robotics
Large-scale closed-loop RL in driving simulation
Large-scale RL training infrastructure (Ray preferred)
Passion for next-generation, scalable autonomy and robotics for real-world systems
Strong engineering and research skills and the 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 our 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