We are building next-generation end-to-end autonomous driving systems powered by reinforcement learning.

You will work on applying RL in closed-loop, safety-critical environments, leveraging large-scale simulation and real-world driving data to improve safety, comfort, and robustness.

• Train and deploy RL policies in closed-loop driving environments
• Scale RL training using massively parallel simulation systems
• Design and optimize reward functions for complex driving behaviors
• Improve sim-to-real transfer for real-world robustness
• Collaborate with cross-functional teams to integrate models into production systems

Requirements

Core Technical Skills

• Proficiency in modern RL algorithms: DQN, PPO, SAC, TD3, etc.
• Proficiency in modern RLHF algorithms: PPO, DPO, GRPO, etc.
• Hands-on experience training reward models and finetuning LLM/VLM/VLA
• Knowledge of distributed RL training at scale
• Proficiency with massively parallel simulation environments
• Knowledge of sim-to-real transfer techniques and domain randomization
• Proficiency in Python, comfortable with C++
• Proficiency in deep learning frameworks such as PyTorch
• Experience with distributed training frameworks (Ray, Horovod, etc.)
• Knowledge of model optimization (quantization, pruning) and CUDA is a plus
• Knowledge of traffic rules, driving behavior modeling

Preferred Qualifications

• Publications in top-tier venues (ICML, NeurIPS, ICLR, CVPR, ICCV, ECCV, ICRA, IROS, etc.)
• Open-source contributions to RL libraries or autonomous driving projects
• Previous experience with LLM fine-tuning using RLHF
• Knowledge of safe RL, interpretable AI, or robustness techniques
• Familiarity with autonomous vehicle regulations and safety standards