Manager II, Machine Learning Engineering

Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.

Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.

Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.

Pinterest is a platform where > 500 million users come to discover ideas, get inspired, and turn dreams into action. Every time a user opens Pinterest, Homefeed is what greets them. For over half a billion people each month, your work will help define what they see, how they feel, and how they connect with what matters to them. As the Manager II, Machine Learning Engineering for Homefeed Retrieval Modeling, you sit at the heart of this mission, propelling both technology and people forward.

Pinterest Homefeed is known for being at the forefront of recommendation system technologies, helping to pioneer innovative breakthroughs that are scalable to positively impact the Pinner experience. Retrieval is where the recommendation journey starts, and that is the scope of the team you’ll lead. More specifically you’ll own:

• All of the retrieval sources (candidate generators), including machine learned ones such as two-tower and conditional two-tower
• ML optimization engines to dynamically trigger the different candidate generators
• Signal development and integration in early funnel of recommendation systems to drive growth and retention on the the platform
• Forward-looking investments such as generative retrieval technologies to bring step-function improvements in how contents are recommended
  
  

• →People Leadership: Build, mentor, and empower a diverse team of 8-9 machine learning engineers—focusing on growth, well-being, and career ambitions. Guide high level ICs, nurture rising stars, and foster an environment where curiosity and mastery thrive.
• →Technical Direction: Set the strategy, architecture, and roadmap for machine learning-based solutions at the heart of Homefeed. Bring together state-of-the-art ML techniques, cost-efficiency, and infrastructure excellence to drive discovery.
• →Cross-Functional Collaboration: Partner with product, design, and engineering teams to connect business goals and user needs. Drive the conversation for new product experiences and ensure Homefeed remains best-in-class, always evolving to meet what’s next.
• →Stakeholder Influence: Bridge leadership vision with execution, maintain clarity through change, and build trust during periods of transformation. Provide senior leadership with visibility into the team’s impact across Pinterest and the industry.
• →Culture Development: Model a culture of expertise, inclusivity, and psychological safety. Your guidance sets the tone for the team—balancing stability and evolution as the organization grows.
• →How you may grow:
• →
  • →This role is uniquely positioned for extraordinary growth:
  • →Industry Visibility: Innovate in ML-driven recommendation systems at a scale few companies reach. Your work will shape state-of-the-art practice, inspire the industry, and be recognized not only internally but by millions globally via blog posts and conference paper submissions.
  • →Core Business Value: Homefeed Retrieval directly influences the largest user touchpoint at Pinterest. As the business and platform grows, this role and the team will scale naturally with it.
  • →Broad Technical Scope: Homefeed Retrieval influences the largest user touchpoint at Pinterest. You’ll lead across core ML topics—learned retrieval, candidate generator modeling, RecGPT, deep collaboration with Principle-level engineers, other organization leaders, and more.
  • →Mentorship and Legacy: Build a mentoring legacy not just for your direct team of 8+ engineers (including Staff-level MLEs), but also by shaping new leaders and influencing peers across the company.
    
    

• 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.

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.
• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.

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