Research Engineer - Reinforcement Learning, Self-Driving

Responsibilities

• Conduct research on reinforcement learning (RL) and training infrastructure, including large-scale self-play RL, VLA post-training, and closed-loop RL based on neural simulation.
• Collaborate with Research Scientists and interns to produce high-quality research publications for top-tier conferences.
• Partner with engineering teams across ADAS, data, and simulation to deploy end-to-end algorithms for mass production vehicles and neural simulation tools.

Requirements

• Hands-on experience in self-play RL, imitation learning, VLA post-training, or large-scale closed-loop RL in driving simulation.
• Experience with large-scale RL training infrastructure (Ray preferred).
• Proficiency in Python, PyTorch, computer vision, robotics systems, and distributed machine learning model training.
• Strong engineering and research skills with the ability to work both independently and collaboratively.

Preferred Qualifications

• MSc or PhD in machine learning, computer vision, or a closely related field with applications in autonomy and robotics.
• Industry experience in relevant fields, particularly self-driving applications.
• Passion for building and shipping customer-focused software frameworks or tools.

Benefits

• Competitive compensation including base salary and equity (options and/or RSUs).
• Comprehensive health, dental, vision, life, and disability insurance.
• 401k retirement benefits with employer match.
• Learning and wellness stipends, and paid time off.

About the Company

Applied Intuition is powering the future of physical AI. Founded in 2017, the company creates the digital infrastructure needed to bring intelligence to every moving machine on the planet, servicing the automotive, defense, trucking, construction, mining, and agriculture industries.