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