AI Engineer (World Models)

Foundation•San Francisco, CA

About The Position

We are building a general-purpose humanoid that must understand and navigate the physical world — and that requires a dedicated engineer to architect the internal models that make that possible World models are the cognitive backbone of our robot; without them, the humanoid cannot plan, predict, or adapt to novel environments We are at an inflection point where our hardware is ready — now we 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 — you have 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, with 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 — and the ability to close the sim-to-real gap in learned representations
Able to read and implement from recent arXiv papers with minimal overhead — you are comfortable turning a research prototype into a tested, integrated system