Product Manager, Core Models

Responsibilities

• Translate user and product goals into clear model requirements, system architecture choices, and research priorities across query understanding, indexing, retrieval, ranking, tool boundaries, data, training, inference, and evaluation.
• Build closed learning loops that turn product usage, explicit feedback, and other user signals into datasets, evaluations, experiments, training priorities, and launch decisions.
• Define success across offline evaluations and online product metrics, balancing model quality, usefulness, latency, safety, reliability, and cost.
• Partner closely with post-training research, applied product engineering, Model Design, and Data Science to integrate capabilities into the mainline model stack.
• Create reusable platforms and operating systems for evaluation, experimentation, and signal collection so that new capabilities improve faster over time.
• Use concrete product failures and emerging user needs to identify gaps, form hypotheses, and shape the next wave of research and product investment.

Requirements

• Deep expertise in product management or closely related experience, including ownership of technically complex products or platforms.
• Deep fluency in one or more relevant domains: search and information retrieval, recommendation or personalization systems, ML platforms, large-scale data systems, model evaluation, or AI product infrastructure.
• Know how to pair offline evaluation with online experimentation and user signals, and can distinguish a useful metric from a convenient one.
• Earn the trust of researchers and engineers through technical depth, crisp judgment, and a willingness to engage directly with the details.
• Combine consumer product taste with systems rigor, and care about both the quality people feel and the infrastructure that makes it repeatable.
• Move quickly in ambiguous environments, communicate directly, and help teams make sound decisions without waiting for perfect information.
• Care deeply about how increasingly capable models affect people, and bring humility, judgment, and a strong sense of responsibility to the work.

Nice-to-Haves

• Experience with AI model development, evaluation systems, or large-scale ML infrastructure.