This team is the part of AI Platforms and Infra that powers the tooling and systems behind recommendation models across Monetization, Instagram, and Facebook. The mission is simple but high-impact: help ML engineers move fast - taking an idea from authoring to training to production inference with high confidence. You will own the product strategy across four areas that define how quickly and reliably Meta ships recommendation models: MLE productivity - reduce friction in ML workflows and make iteration meaningfully faster Research-to-production velocity - shorten the cycle time from experimentation to reliable launches Quality and reliability - build tooling and systems that are robust, scalable, and trustworthy across the ML stack Foundational infrastructure - deliver capabilities spanning data, developer experience, training, and inference that unlock new recommendation quality
Responsibilities
Own the end-to-end product strategy for Meta's recommendation systems infrastructure - spanning model authoring, training, inference, and the developer experience that ties them together
• Define how ML engineers across Monetization, Instagram, and Facebook build, train, and ship recommendation models - your decisions directly affect the quality and velocity of these products Drive MLE productivity by identifying the highest-friction points in ML workflows and building tooling that removes them
• Partner with engineering and research leaders to translate infrastructure capabilities into measurable improvements in model quality, training efficiency, and launch reliability
• Build and lead a team of product managers, setting clear charters across the ML stack and creating an environment where PMs develop conviction and ship independently
• Sequence investments across a broad portfolio - making deliberate tradeoffs between near-term MLE pain points and foundational infrastructure bets that compound over time
• Establish alignment across AI Infra, recommendation teams, and product surfaces whose competing needs shape infrastructure priorities
• Define and instrument the metrics that tell you whether MLEs are actually moving faster, models are launching more reliably, and infrastructure investments are paying off
• Lead a team through the ideation, technical development, and launch of innovative products
• Establish shared vision across the company by building consensus on strategies and priorities leading to product execution
• Drive product development with a team of world-class engineers and designers Integrate usability studies, research and market analysis into product requirements to enhance user satisfaction
• Define and analyze metrics that inform the success of products Understand Meta's strategic and competitive position and deliver products that are recognized as best in the industry
• Maximize efficiency in a constantly evolving environment where the process is fluid and creative solutions are the norm
• Attract, build, manage, and develop a talented team of product leaders with a broad range of experiences, perspectives, approaches, and backgrounds
• Manage multiple products and priorities, scale teams, and ensure org is effective, healthy and set up for success by establishing clear and measurable goals
Minimum Qualifications
• 12+ years of experience in Product Management and/or equivalent relevant experience
• 12+ years of experience working collaboratively with engineering, design and user research teams
• 8+ years of experience hiring, managing, and developing both individual contributors and senior individual contributors
• Critical thinking/analytical leadership experience
• Strong written and verbal communication - ability to distill complex technical topics into clear documents for executive audiences
• BA/BS in Computer Science or related field
Preferred Qualifications
• Deep familiarity with ML systems - training infrastructure, model serving, feature engineering, or ML data pipelines at scale
• Experience building developer tools or platforms for ML engineers, data scientists, or applied researchers
• Understanding of recommendation systems and the tradeoffs in ranking, retrieval, and personalization at scale
• Track record of managing infrastructure products where the "user" is an internal engineer - comfort with developer experience as a product discipline
