Research Scientist, World Models - Policy Training and Evaluation

About the position Responsibilities • Develop and refine world models that support realistic and diverse counterfactual reasoning, scenario generation, and policy rollout. • Ensure that world models are compatible with and useful for reinforcement learning, imitation learning, and offline policy evaluation techniques. • Design methods to synthesize high-risk or edge-case scenarios from world models, enabling robust stress-testing of autonomous policies. • Explore techniques such as latent-space simulation, world model distillation, differentiable simulation, and closed-loop evaluation to improve policy development and evaluation pipelines. • Partner with researchers in world modeling, planning, and safety evaluation to co-develop aligned architectures and learning objectives to ensure that learned models accurately capture agent-environment dynamics relevant to long-horizon planning and safety-critical decision-making. • Publish high-quality research and contribute to the community through open-source tools, benchmarks, and conference participation. Requirements • PhD in Computer Science, Robotics, Machine Learning, or a related field. • Strong background in at least two of the following areas: World models or model-based reasoning in dynamic environments, World model adaptation and fine-tuning, Offline RL or imitation learning, Model-based reinforcement learning (MBRL), Simulation-to-reality transfer, or Policy evaluation and safety assurance. • A track record of high-quality publications in ML or robotics venues (e.g., ICML, ICLR, NeurIPS, CoRL, RSS). • Familiarity with latent dynamics models (e.g., Dreamer, PlaNet, MuZero). • Understanding of uncertainty modeling, generalization, and robustness in learned environments. • Experience evaluating autonomous vehicle policies in simulation and real-world settings. • Experience in building or applying models for downstream evaluation of autonomous systems. • Proficiency in Python and ML frameworks (e.g., PyTorch, JAX). Benefits • 401(k) eligibility • various paid time off benefits, such as vacation, sick time, and parental leave • annual cash bonus structure Apply tot his job Apply To this Job