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AfterQuery is an applied research lab that curates data solutions for foundation model development, serving frontier AI labs with the mission of delivering the best data to power the best models. The company is YC's fastest unicorn, valued at $3.2 billion, and is backed by leading investors including Altos Ventures, BoxGroup, Y Combinator, and angels from Google DeepMind, OpenAI, Anthropic, Meta Superintelligence Labs, and Microsoft AI.
In this role, you will own the execution of research programs spanning post-training, enterprise post-training, fellowships, evaluation releases, and partner research. Your primary responsibility is to make ambitious research programs move by translating open-ended research goals into clear, actionable plans. You will keep researchers, engineers, SPLs (Senior Program Leads), and external partners aligned while surfacing critical decisions and blockers. This is not a traditional project-management role—you must understand the technical work deeply enough to reason about experiments, datasets, evaluations, training pipelines, and research tradeoffs. Your impact will come from creating just enough structure for the team to run more programs, make better decisions, and ship high-quality work faster.
Key responsibilities include:
- Own planning and execution across post-training, enterprise post-training, fellowship, and public evaluation programs
- Translate ambiguous research objectives into milestones, owners, dependencies, success criteria, and decision points
- Maintain a trustworthy view of program health, including experiment progress, risks, blockers, partner dependencies, and upcoming releases
You will work alongside a founding team with experience from Citadel Securities, Meta, Google, Silver Lake, and Morgan Stanley. This is a rare opportunity to join a company at a defining moment in AI and to own and architect core infrastructure systems that power the platform from the ground up.
REQUIREMENTS:
- 2-4 years of project/program management experience
- Experience leading complex technical programs across research, machine learning, engineering, or data organizations
- Working knowledge of the LLM development lifecycle, including data creation, SFT, RL-based post-training, and evaluation
- Strong communication skills and ability to turn messy technical information into clear decisions and next steps
