Research Engineer - Reinforcement Learning, Self-Driving

The Robot Age; Sunnyvale (CA); Hybrid; USD 140,000 - 190,000; Full time

Job summary

Applied Intuition in California is seeking a research engineer focused on reinforcement learning for self-driving systems. You will translate cutting-edge ideas into reliable code, build evaluation loops, and collaborate with researchers to push the boundaries of autonomy.

This role emphasizes infrastructure, simulation, and physical AI, with responsibilities spanning experiments, tooling, and measuring behavior quality to ensure deployable autonomy.

Qualifications

• Strong engineering background in ML, reinforcement learning, robotics, or autonomy.
• Ability to implement research methods and debug complex model behavior.
• Experience with experimentation, evaluation, and production-aware research workflows.

Responsibilities

• Implement reinforcement-learning methods for autonomous driving research.
• Build experiments and tooling that help compare behavior policies.
• Work with researchers on physical AI systems spanning simulation and real-world autonomy.
• Translate research ideas into reliable code and repeatable evaluation loops.

Skills

ML

Reinforcement Learning

Robotics

Autonomy

Job description

What the role actually is

Applied Intuition is hiring research engineers for its AI research group. This opening focuses on reinforcement learning for self-driving systems, with the broader team working across end-to-end autonomy and robotic generalist research.

The role is research engineering rather than product design, but it fits the board's autonomy-research lane because evaluation, simulation, and behavior quality are central to whether autonomy becomes usable in the world.

What you would work on

• Implement reinforcement-learning methods for autonomous driving research
• Build experiments and tooling that help compare behavior policies
• Work with researchers on physical AI systems spanning simulation and real-world autonomy
• Translate research ideas into reliable code and repeatable evaluation loops

What they are asking for

• Strong engineering background in ML, reinforcement learning, robotics, or autonomy
• Ability to implement research methods and debug complex model behavior
• Experience with experimentation, evaluation, and production-aware research workflows
• Interest in self-driving systems and physical AI infrastructure

Why this one is worth a look

The interesting piece here is the infrastructure context. Reinforcement learning for autonomy only matters if teams can measure, compare, and trust behavior changes; Applied's business gives that research a direct path into autonomy tooling.