Member of Technical Staff - Robotics Research Lead

We are an AI research lab building foundational intelligence for the physical world.

Our models are designed to solve complex real-world challenges, powering everything from intelligent robots to next-generation wearables and media.

We work at the intersection of world models, video generation, and robotics, building systems that represent complex environments, reason about objects and dynamics, and translate high-level AI intentions into smooth, efficient, safe, and responsive actions across a variety of robotic embodiments.

You will collaborate closely with ML researchers developing multimodal world models and generative systems, and with hardware teams and partners to ensure robotic platforms, sensing, and system dynamics behave optimally in real-world operation.

What You’ll Do

- Build the Foundation: Collaborate with our world model team to build state-of-the-art training and simulation platform for robotics.

- Build the "World": Develop persistent 3D/4D scene representations that maintain temporal consistency.

- Drive Intelligence: Unlock advanced robotics planning and decision making through our in-house, cutting-edge world models.

- Sim2Real: Ensure sensing and system dynamics perform reliably in high-stakes, real-world operations, where models think, simulate, and act.

- Push Boundaries: Partner with top ML researchers to innovate on generative models and physical AGI.

AREAS OF INTEREST

We are particularly excited about candidates working on topics such as:

- World models for embodied systems

- 3D / 4D generative models

- 3D reconstruction and scene understanding

- Embodied AI

- Object-level / semantic SLAM

- Embodied AI

- Multimodal AI

WHAT WE’RE LOOKING FOR

- Background in robotics, or computer vision

- Experience with 3D perception, scene representations, or world models

- Experience working with robotic systems, simulation, or real-world platforms

- Strong programming skills (Python, C++, or similar)

Ability to work across machine learning, perception, and robotics