Engineering Manager II, Machine Learning – User Understanding

We're seeking an Engineering Manager to lead our User Understanding team. This team builds and maintains the data and backend systems that drive user modeling, personalization and recommendations,impacting hundreds of millions of users every day.

As an Engineering Manager, you will set technical vision, empower a talented group of engineers, and collaborate closely with Machine Learning, Product, and Data Science to unlock better user experiences.

Key responsibilities include:

• Leading and mentoring a team of experienced machine learning engineers in developing advanced user understanding models and systems
• Collaborating cross-functionally with partners in Product, Data Science, and Engineering to define strategy, set priorities, and effectively integrate user insights into core systems and product features
• Driving experimentation and adoption of new user understanding models with product teams across Pinterest, ensuring measurable end-to-end impact on key metrics
• Managing project execution and stakeholder communication, including roadmap planning, technical decision-making, risk mitigation, and progress updates to achieve business goals
• Providing thought leadership in user modeling and recommender systems by setting a long-term technical vision and advancing the state-of-the-art in the field
• Fostering a collaborative, inclusive, and high-performing team culture

Requirements include:

• 6+ years of industry experience in applied machine learning or closely related roles, with experience developing large-scale machine learning for recommendation or ads systems
• 2+ years of people management experience leading engineering teams
• Demonstrated ML expertise with a proven track record of impactful solutions and a deep understanding of large scale distributed systems
• Strong technical leadership, including a passion for driving technical direction, system architecture, and designing robust, scalable ML solutions

Nice to have:

• 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