Key skills
Machine learningRecommender systemsSearch systemsAds systemsEmbedding-based retrievalCost-efficiency in MLProduct-facing ML solutionsAI coding assistantsLLM-powered productivity toolsStrategic prioritizationSQLAIMachine LearningMLLLMLeadershipStakeholder Management
About this role
Pinterest is a platform where millions of users come to discover ideas and get inspired. As the Manager II, Machine Learning Engineering for Homefeed Retrieval Modeling, you will lead a team of machine learning engineers to drive innovative solutions that enhance user experience and engagement.
Responsibilities:
- Build, mentor, and empower a diverse team of 8-9 machine learning engineers—focusing on growth, well-being, and career ambitions
- Set the strategy, architecture, and roadmap for machine learning-based solutions at the heart of Homefeed
- Partner with product, design, and engineering teams to connect business goals and user needs
- Bridge leadership vision with execution, maintain clarity through change, and build trust during periods of transformation
- Model a culture of expertise, inclusivity, and psychological safety
Requirements:
- Depth in ML: You bring deep expertise in large-scale production recommender, search, or ads systems. You know the landscape of embedding-based retrieval, experimentation, cost-efficiency, and product-facing ML solutions
- Leadership Maturity: 8+ years of experience, including 1+ year managing high-performing ML teams (10 or more engineers), and a proven record of hiring, empowering, and developing technical talent
- Strategic Instinct: You balance ambition and pragmatism, thoughtfully prioritizing investments in foundational and experimental work. You keep business goals and user experience front and center, never losing sight of impact
- Collaborative Spirit: Experienced in cross-functional stakeholder management, you help diverse teams navigate change, achieve shared goals, and thrive through ambiguity
- Growth Orientation: Whether guiding senior ICs or partnering with peers, you listen deeply, adapt thoughtfully, and help people stretch in pursuit of excellence
- AI Efficiency: Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring. Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration