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Applied Intuition

Research Engineer - Reinforcement Learning, Self-Driving

126k – 423kSunnyvale, CAOnsiteEntry level
Summary

Conducts research on reinforcement learning for self-driving cars and robotics, develops large-scale RL training infrastructure, and deploys algorithms to production systems. Requires hands-on RL experience, PyTorch/Python proficiency, and strong research skills.

About the role

Responsibilities

  • Conduct research on reinforcement learning (RL) and its training infrastructure, including 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.

Requirements

  • Hands-on experience in at least one of: 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 ability to work independently and collaboratively.
  • Technical experience in: Python, PyTorch, computer vision, robotics systems, and distributed machine learning model training.

Nice to Have

  • 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.

Compensation

Base salary range: $126,000 - $423,000 USD annually, plus equity and benefits.

Skills
Reinforcement LearningPyTorchPythonComputer VisionRayImitation LearningDistributed Machine LearningRoboticsSelf-Play RLVLA
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