Research Member of Technical Staff- Reasoning

What You'll Do

• Research and develop methods for multi-step reasoning and planning grounded in embodied world models
• Design architectures and training strategies that improve compositional generalization and long-horizon prediction
• Explore chain-of-thought reasoning, process reward models, and test-time search in the context of robotic control
• Build evaluation benchmarks for reasoning and planning capabilities applied to physical tasks
• Investigate how world model rollouts can enable planning and decision-making at inference time
• Collaborate with pre-training and post-training teams to integrate reasoning capabilities into the full model pipeline
• Publish and present work at top-tier venues (especially valued for RS track)

What We're Looking For

• Strong background in reasoning, planning, or search with large models
• Deep understanding of sequence modeling, transformer architectures, and generative models
• Experience with test-time compute methods (beam search, MCTS, self-consistency, verifiers, etc.)
• Strong research taste and ability to identify high-leverage directions
• Fluency with PyTorch or JAX and ability to implement and iterate on research ideas end-to-end

Staff-Level Candidates

• Define technical direction and drive research strategy independently
• Execute complex projects with strong fundamentals and growing scope

Senior/MTS Candidates

• Execute complex projects with strong fundamentals and growing scope

Nice To Have (But Not Required)

• PhD in ML, Robotics, or a closely related field
• Publication record at NeurIPS, ICML, ICLR, CoRL, or related venues
• Prior work on reasoning in LLMs (chain-of-thought, process reward models, search-based methods)
• Experience with model-based planning or hierarchical reinforcement learning
• Familiarity with long-horizon prediction, video generation, or world model rollouts
• Experience with embodied AI or robotic planning problems