Staff Research Scientist, Reinforce Learning
Core
Building next-generation AI systems for autonomous driving at the intersection of machine learning, simulation, robotics, and real-world deployment.
Role type
Staff Research Scientist (Embodied AI / Reinforcement Learning)
Builds
Core innovations in embodied AI including world models, reward modeling, spatial intelligence, and scalable decision-making systems for autonomous vehicles.
Domain
Autonomous driving, Embodied AI, Robotics, Simulation
Deliverable
production ML models
Required skills
Reinforcement Learning, World Modeling, Spatial AI (SLAM/SfM, depth estimation), Foundation Models (Transformers, MoE), Generative Modeling (Diffusion, Autoregressive), Python, PyTorch, Large-scale dataset evaluation, Top-tier conference publications
Preferred skills
Autonomous driving experience, Large-scale training frameworks (FSDP, DeepSpeed, JAX), Sim-to-real transfer, Open-source contributions
Technologies
PyTorch, Transformers, Diffusion models, Autoregressive models, SLAM, SfM
Responsibilities
Develop World Models and Planners for realistic simulation; Advance Reinforcement Learning and Reward Modeling frameworks; Develop Geometric Foundation Models for 3D spatial understanding; Enable Cross-Embodiment Robotics; Conduct empirical research on Scaling laws and Sim-to-real transfer; Define Evaluation Frameworks and Benchmarks for driving performance.
Seniority
Staff, hands-on IC with strategic impact
