Odyssey is pioneering the next frontier of artificial intelligence: generative worlds. By learning from the real-world, they’re training a new kind of visual generative model capable of generating cinematic worlds that you can direct—from scenery and characters to lighting and motion. Odyssey’s mission is to reinvent film, gaming, and more.

Odyssey was founded in late 2023 by Oliver Cameron (Cruise, Voyage) and Jeff Hawke (Wayve, Oxford AI PhD), two veterans of self-driving cars and AI. They’ve since recruited a world-class team of ML researchers from Cruise, Waymo, Wayve, Tesla, Microsoft, Meta, and NVIDIA; lead computer graphics researchers from EA, Ubisoft, and Valve; and technical artists behind Hollywood blockbusters like Dune, Godzilla, Avengers, and Jurassic World.

Odyssey has raised venture capital from GV, EQT Ventures, Air Street Capital, DCVC, Elad Gil, Garry Tan, Soleio, Jeff Dean, Kyle Vogt, Qasar Younis, Guillermo Rauch, Soumith Chintala, and researchers from OpenAI, DeepMind, Meta, Midjourney, and Pixar.

Who You Are

• You have 5+ years of experience in ML data operations, logistics, or a related field.
• You have a proven track record of constructing large, leading ML datasets.
• You understand how to iteratively improve a dataset to best optimize for ML model performance.
• You have strong project management skills, and have demonstrated the ability to decisively prioritize and coordinate across multiple teams.
• You have excellent communication and interpersonal skills, with experience working closely with researchers.
• You are familiar with state-of-the-art data tooling, data management, and machine learning infrastructure.

What You’ll Do

• You’ll lead the end-to-end logistics of data acquisition and collection, ensuring that we’re hitting dataset volume targets for training runs.
• You’ll own our real-world data collection efforts, building and leading a team of data collectors that travel the world to collect valuable real-world data.
• You’ll own our synthetic data collection efforts, building and leading a team of artists who construct large-scale synthetic training data.
• You’ll work closely with ML researchers and external vendors to detail data requirements, enabling large-scale data sourcing, labeling, and quality control.
• You’ll develop and enforce stringent data quality standards, implementing QA processes to ensure that data meets ML-readiness benchmarks.