Odyssey builds, secures and integrates capabilities and technologies to meet tomorrow’s mission requirements. We enhance operational readiness through comprehensive program support. Backed by a16z, General Catalyst and GV.
About the role
Learn what makes large real-time world models tick. Understand how data, architecture, scale, and diffusion algorithms interact. Run scaling studies and use them: fit scaling laws over model size, data, and compute, and let them pick the next large run rather than intuition alone.
What they're looking for
- 2+ years of software engineering experience, with significant work in ML performance
- 4+ years of ML engineering experience, or a PhD in a related field
- Hands-on experience training large models, pretraining at multi-node scale, and the debugging that comes with it
- Comfortable reasoning about scale: scaling laws, compute and data budgets, and what a small-scale ablation does and does not predict
- A holistic ML engineer — happy to move between data, model, systems, and evaluation, and to own the whole path from an idea to a shipped model
- Track record of owning projects end to end
More about this role
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.
The right person will have a deep interest in building large foundation world models, and in the scaling laws that tell you which ones are worth building. You will want to train them from scratch, at the scale that takes: runs across thousands of GPUs, data mixtures measured in years of...
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