Member of Technical Staff, Diffusion World Models & Robotics

Who we are Odyssey is an AI lab pioneering general world models: causal, multimodal systems that learn to predict and interact with the world over long horizons. This foundational technology promises to revolutionize robotics, science, healthcare, education, gaming, defense, and beyond. Odyssey’s founders previously pioneered the most complex application of physical AI: self-driving cars. They’ve now brought together a world-class research team from DeepMind, Tesla, Waymo, Meta, Apple, and Wayve, who have made significant contributions to language models (DeepMind Gemini), video models (DeepMind Veo), world models (Wayve GAIA), and autonomous systems (Tesla FSD). Odyssey has raised significant venture capital from GV, Amazon, AMD, EQT, NVIDIA, Natural Capital, In-Q-Tel, Elad Gil, Jeff Dean, Guillermo Rauch, Garry Tan, Kyle Vogt, and researchers from OpenAI, DeepMind, MSL, Recursive, and Thinking Machines.

What we're looking for We are looking for people with a deep interest in improving machine learning algorithms at the intersection of diffusion world models and robotics. Interactive, action-conditioned world models are a cutting-edge research area that is not yet mature. You will be working at the cusp of what’s possible, using world models as simulators, feature extractors, and training grounds for real robot policies and model-based RL approaches. Most new experiments here will fail; your focus will be on maximally learning from failed experiments to increase the chances of eventual success on real hardware.

What you'll do Learn what makes interactive world models tick. How data, diffusion backbones, action conditioning, and downstream policies interact. Implement state-of-the-art diffusion and robot-learning algorithms, and design losses, reward functions, and reinforcement / preference-based fine-tuning for interactivity and policy performance. Run a high cadence of ablations across WM and policy, from architecture and feature taps to data mixes and conditioning schemes. Build the bridge to real robots: data pipelines, WM fine-tuning, and policy deployment onto Robots-aaS and design partner systems (remote and face-to-face). Exploit the latest features on modern GPUs to increase training and inference efficiency. Take ownership of the full ML stack, including core frameworks that Odyssey researchers and product engineers rely on.