You'll own two connected but distinct areas. First, you'll decide which new scientific domain capabilities the Lila model should gain and how to build them — the right mix of reinforcement-learning environments and supervised fine-tuning (SFT) data — prioritized against our Target Product Profiles (TPPs) and customer needs. Second, you'll own the roadmap and end-to-end delivery of fine-tuned Lila model variants for priority platform and commercial use cases, including the training data, evals, and release criteria needed to ship them. These capability priorities also feed the AI Research team's core-model training cycle as a structured input, though that cycle itself sits outside this role's direct ownership. You'll sit within the Research Product Manager function and work side-by-side with AI Research, joining their sprint planning and standups, to ensure the right capabilities are built and upstream into the Lila foundation model.
What You'll Be Building
Own and maintain the prioritized backlog of training-capability packages (reinforcement-learning environments + SFT data), weighing Lila's TPPs, customer/commercial needs, and quarterly Lila model training needs
Own the roadmap and delivery of fine-tuned Lila model variants for priority platform and commercial use cases: define the required training data, strategic data gaps, evals, and release criteria
Coordinate the handoff of validated fine-tunes to the app and platform teams
Serve as a structured input into the AI Research team's core-model training cadence, which sits outside this role's direct ownership
Partner with AI Research (training-pipeline owners, evals, and data-mix leads) to translate prioritized capabilities into clear, well-specified requirements
Represent customer- and commercial-driven capability requests, and translate them into concrete capability asks the research team can execute against
Integrate into AI Research sprint planning and standups to track delivery and unblock cross-team dependencies
Define and track release/acceptance criteria for new capabilities in partnership with the evals workstream, so shipped work can be validated before it informs fine-tuning or Lila model training
Qualification
Strong cross-functional operator
Required
Experience as a Product Manager on ML/AI products, ideally with exposure to LLM training, fine-tuning, or RL-based capability development
Experience owning delivery of a model or product variant for external or commercial customers, including defining data, evals, and release criteria
Demonstrated ability to build and defend a prioritized backlog against competing scientific, research, and commercial demands
Strong cross-functional operator, comfortable partnering closely with a research organization (joining sprint planning/standups, not just handing off requirements)
Track record translating ambiguous or evolving priorities into a clear, sequenced set of deliverables
Preferred
Familiarity with capability-based training approaches for LLMs (RL environments, SFT data generation, evals)
Experience partnering with ML/AI research teams on eval design and release criteria
Background in a scientific or technical domain relevant to Lila's mission
