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

We are hiring a AI Research Scientist, Reinforcement Learning (LLM) and Post-Training, specializing in reinforcement learning to advance post-training and interactive learning for large generative models applied to demanding engineering and hardware-adjacent tasks (code, optimization, tool use, and long-horizon decision making). You will invent and analyze RL algorithms—policy optimization, preference-based methods, exploration, credit assignment, and reward modeling—run rigorous empirical studies, and partner with infra and product teams to land methods that improve measurable task success without sacrificing stability or safety.

THE PERSON: You publish and ship. You are fluent in both RL theory and the practical path from ablation to production-scale training. You care about reward misspecification, variance reduction, and evaluation that reflects real constraints—not only toy environments. KEY RESPONSIBILITIES: Research and develop RL methods for post-training LLMs and code models on structured engineering tasks with verifiable or preference-based feedback Design reward models, curricula, and off-policy or on-policy training recipes suited to sparse, noisy, or expensive labels from experts and simulators Characterize failure modes (reward hacking, degenerate policies, instability) and propose mitigations grounded in experiments Collaborate with RL infra engineers to scale training; define interfaces for rollout generation, logging, and reproducibility Publish at top venues (e.g.