Manager II, Machine Learning Engineering

Job Description

About Pinterest: 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 What you’ll do: 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, nur

Required Skills