AI Research Scientist, Reinforcement Learning (LLM) and Post-Training

Develop reinforcement learning methods to advance post-training and interactive learning for large generative models used in engineering and hardware-adjacent tasks. Invent and analyze RL algorithms such as policy optimization, preference-based methods, exploration, credit assignment, and reward modeling. Run rigorous empirical studies, characterize failure modes, and collaborate with RL infrastructure and product teams to improve measurable task success while maintaining stability and safety.