AI Resident
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- Work arrangement
- Not stated
- Employment
- FullTime
- Stated compensation
- Not specified
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Requirements
1 with evidence8 unclear or unstated- Remote policy
- onsite
“Our staff is expected to be in office at least three times a week.”
See matched passage in original description ↓ - Employment type
- FullTime
9 fields not stated in this posting
- Employer sponsorship
- Not specified
- Geographic scope
- Not specified
- Degree requirement
- Not specified
- Security clearance
- Not mentioned
- Timezone / hours
- Not specified
- Citizenship requirement
- Not mentioned
- Minimum experience (years)
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Original job description
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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 AI Residency is built for someone who's likely post-MS or equivalent, with real evidence they can do serious ML work, but who isn't yet operating at the level of independent, publication-driving science. You may not have direct experience in world models, generative video, multimodal learning, robotics, or multi-agent systems specifically, what matters more is that you're sharp, relentless, and capable of building that expertise fast, embedded alongside people who already have it. Residents who excel over the course of the program are extended full-time offers to join Odyssey permanently.
What you'll do
Work embedded within one of our research pods on a live project, whether that's world simulation, multimodal generation, robotics, or multi-agent systems
Take on real, scoped pieces of our roadmap under the guidance of senior researchers and engineers, not isolated side projects
Run experiments, build and debug training and evaluation pipelines, and iterate quickly on ideas with real compute behind them
Contribute to the models we ship, and see your work show up in systems used by real people
Learn the full loop of frontier research: hypothesis, implementation, experimentation, and honest evaluation of what worked and what didn't
Who you are
Post-MS or equivalent experience, with some demonstrated evidence of ML research ability (coursework, a thesis, prior internships, competitive results, open-source contributions, or similar)
Strong fundamentals in machine learning and software engineering, and genuinely fast at picking up new subfields
Not necessarily experienced yet in world models, generative video, multimodal learning, robotics, or multi-agent systems, but hungry to build that expertise on the job
Comfortable with ambiguity and a fast-moving, high-ownership environment; you don't need everything specified for you to make progress
Able to work in person at one of our offices in Palo Alto, London, or Zurich for the duration of the residency
Genuinely motivated by Odyssey's mission: discovering the GPT-3 moment for world models
The Process
As part of your application, please submit a short research proposal for a project you’d like to pursue. This should be at most a page and it is intended to help scope projects. Projects will be refined with successful applicants and the Odyssey research team.
Our staff is expected to be in office at least three times a week. A lot of our progress comes from working through problems together, and we want you in the room for that.
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