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 behaviours Improve sim‑to‑real transfer for real‑world robustness Collaborate with cross‑functional teams to integrate models into production systems 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 randomisation 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 optimisation (quantisation, pruning) and CUDA is a plus Knowledge of traffic rules, driving behaviour modelling 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 #J-18808-Ljbffr