Data Program Manager

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 Data Program Manager is the connective tissue between research, vendors and the data flywheel. You are responsible for understanding our data needs by being deep in the frontier research space for multi-model methodologies and arxiv papers. You are responsible for presenting a credible data roadmap to the research, applied ML, and leadership and keeping stakeholders abreast of the pipeline and plans. You also own inbound

requirements and turn those into a backlog the data-ops team can execute against. The right person thinks in processes, has strong opinions, and can hold both people (internal and external) and plans accountable.

What you'll do Run data plans with loose

requirements from Research. Turn fuzzy, disparate data requests into scoped, sequenced acquisition plans with quality bars, timelines, and clear data parameters. Manage a multitude of vendors across verticals and data types, maintaining relationships and negotiating commercial terms favorable to Odyssey. Manage the human teams (experience with contractors a big plus) that acquire proprietary data and establish and enforce quality measures. Stand up and own the weekly data-needs review and forecast. Force-prioritize when necessary and surface the trade-offs to leadership. Embed in research planning. Flag data risks four to six weeks ahead of when they would hit a model run. Define and track quality and throughput metrics across active data programs. Redline and QA batches that slip, and close the loop with vendors. Partner with Research on data