AI Engineer (World Models)

Foundation • San Francisco, CA

22h

About The Position

We are building a general-purpose humanoid that must understand and navigate the physical world. This role requires a dedicated engineer to architect the internal models that make this possible. World models are the cognitive backbone of our robot; without them, the humanoid cannot plan, predict, or adapt to novel environments. Our hardware is ready, and we now need the intelligence layer to match it. The gap between a robot that executes fixed commands and one that truly reasons about its environment is a world model; we are hiring to close that gap. As we scale to real-world deployment, our humanoid needs to generalize across unstructured, unpredictable settings — something only a robust world model can enable. This hire will directly shape the core intelligence architecture of our platform before it becomes locked in at scale.

Requirements

• Hands-on experience building world models, model-based RL, or predictive world simulators using frameworks like PyTorch or JAX.
• Shipped these systems, not just studied them.
• Strong foundation in deep learning architectures relevant to world modeling: transformers, diffusion models, neural radiance fields (NeRF), and variational recurrent state-space models.
• Proficient in Python as a primary research and development language.
• Production-level familiarity in C++ for latency-sensitive inference and real-time robotics integration.
• Experience with robotics middleware and simulation environments — ROS2, Isaac Sim, MuJoCo.
• Able to read and implement from recent arXiv papers with minimal overhead.
• Comfortable turning a research prototype into a tested, integrated system.

Nice To Haves

• Experience with video prediction or future-frame generation models (e.g., RSSM, DreamerV3, UniSim, Genie).

Responsibilities

• Architect the internal models that enable a general-purpose humanoid to understand and navigate the physical world.
• Develop and implement world models, model-based RL, or predictive world simulators.
• Integrate learned representations and close the sim-to-real gap.
• Turn research prototypes from recent arXiv papers into tested, integrated systems.
• Ensure the intelligence layer matches the capabilities of the robot's hardware.
• Enable the humanoid to generalize across unstructured, unpredictable settings.
• Shape the core intelligence architecture of the platform.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed