About the role
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 The right person will have a deep interest in improving machine learning algorithms. Real-time world model simulation is a cutting edge research area that is not yet mature. You will be working at the cusp of what’s possible. Most new experiments in this area will fail, your focus will be on maximally learning from failed experiments to increase the chances of eventual success.
What You’ll Do
- Learn what makes real time world models tick. Understand how data, models and diffusion algorithms interact.
- Implement state of the art ML algorithms, define metrics, and relentlessly iterate on leaderboards.
- Be part of a team that is defining and leading the world model space.
- Exploit the latest features on modern GPUs to increase training and inference efficiency.
- Take ownership of the full ML stack, including the core frameworks that Odyssey researchers and product engineers alike rely on.
Who You Are
- 1+ years of software engineering experience, with significant work in ML performance.
- 2+ years of ML experience.
- Track record of owning projects end to end.
- Not shy to touch any stage of an ML pipeline.
- Proficiency with PyTorch (or TF/JAX).
- Highly experiment driven.
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