Research Scientist, Reinforcement Learning

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