Manager II, Machine Learning Engineering-Homefeed

Pinterest is a platform that inspires creativity and innovation for millions of users. The Manager II, Machine Learning Engineering for Homefeed Retrieval Modeling will lead a team of machine learning engineers to enhance recommendation systems and drive user engagement on the platform.

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