Sr. Research Scientist - Physically-Grounded World Modeling

What You Will Do:

• Research, design, and implement novel algorithms for understanding and modeling complex 3D scenes, with a focus on integrating geometry, semantics, and physics
• Develop and advance generative and physics-informed models capable of predicting realistic future states and dynamic interactions for applications like robotic manipulation
• Design and curate large-scale datasets to enhance model capabilities in physically-grounded and specialized applications
• Create robust 3D representations that capture not just object appearance, but also physical properties, affordances, and causal dymanics
• Incorporate principled uncertainty estimation into your models to ensure they are reliable and aware of their own limitations
• Collaborate with a multidisciplinary team of researchers and engineers to integrate your work into larger systems and build compelling demonstrations
• Share your innovative work with the community through publications at top-tier conferences (e.g., CoRL, CVPR, NeurIPS)
• Mentor junior researchers and engineers, fostering a culture of technical excellence and collaborative innovation

What We Are Looking For

• A PhD, or MS with equivalent research experience in Computer Science, Robotics, or a related field
• 5+ years of relevant post-graduate research or industry experience in robotics, computer vision, computer graphics, or machine learning
• Strong programming skills in Python and/or C++ and deep experience with a major deep learning framework (e.g., PyTorch).
• Experience applying research to robotic systems (e.g., using ROS 2, integrating models on physical hardware).
• A proven track record of creativity and research excellence, demonstrated by publications in leading ML and Robotics conferences.
• Deep expertise in one or more of the following areas: (1). 3D Scene Understanding: (e.g., NeRFs, 3D Gaussian Splatting, 3D reconstruction, geometric deep learning). (2). Generative Modeling: (e.g., Diffusion Models, VAEs, LLMs, Multimodal Models) applied to 3D or dynamic data. (3). Physics-Informed AI: (e.g., Differentiable simulation, Physics-based modeling, System identification).

Bonus Qualifications

• Experience with large-scale model training, including distributed training techniques
• Familiarity with MLOps workflows and tools (e.g., Docker, cloud computing) for large-scale experiments
• Familiarity with causal representation learning or related techniques.

Why Join Us

• We offer a unique environment for those who want to do foundational work with real-world impact, without the constraints of a product-driven roadmap.
• You will have:
• Intellectual Freedom: The latitude to pursue ambitious, long-term research goals in a culture that prioritizes scientific discovery
• Unparalled Resources: Access to extensive GPU compute clusters, unique proprietary datasets, and a world-class robotics lab featuring a variety of commercial and in-house custom robotic platforms.
• A Culture of Balance: We believe groundbreaking work happens when people have the time to think deeply. We offer a more relaxed, collaborative atmosphere with a genuine commitment to work-life balance.
• A Full-Stack Team: You'll be surrounded by, and learn from, world-class experts across the entire robotics stack—from custom hardware design and embedded systems to ML engineering, computer vision, and AI theory.