Staff Machine Learning Engineer

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. The Advertiser and Seller Experience team builds intelligent systems that help Pinterest advertisers and sellers move from insight to action. Our work spans advertiser-facing products such as Ads Manager as well as internal seller productivity tools that help sales teams identify opportunities, prepare customer conversations, troubleshoot campaign performance, and drive advertiser growth. As a Staff Machine Learning Engineer focused on Agentic AI & Recommendations, you will lead the ML strategy and execution for the intelligence layer behind these experiences. You will build recommendation systems, context foundations, and feedback loops that help AI agents understand advertiser and seller goals, surface the right next-best action, and learn from user response over time. This is a high-impact Staff IC role for someone who wants to combine deep recommendation systems expertise with modern agentic AI to shape how Pinterest advertisers and sellers work. What you'll do:  • Lead the design and implementation of large-scale recommendation and decisioning systems that power proactive advertiser and seller guidance across Ads Manager, Pinterest Business Assistant, Pinnacle, and sales productivity workflows. • Build ML foundations for a unified context layer and context agent that transforms campaign, account, performance, market, workflow, and interaction data into reusable signals for agentic experiences. • Own recommendation initiatives end-to-end, from problem framing, label and feedback design, feature pipelines, model development, and offline evaluation through production deployment, experimentation, and monitoring. • Develop evaluation and feedback loops that measure recommendation quality, user trust, action rates, business impact, and failure modes, then use those learnings to continuously improve models and agent behavior. • Apply modern ML techniques such as retrieval and ranking, embeddings, personalization, multi-objective optimization, contextual decisioning, and response modeling to business-critical advertiser and seller workflows. • Use AI to accelerate analysis, prototyping, documentation, and experimentation while applying strong judgment, testing, data validation, and review to ensure correctness, reliability, privacy, and customer trust. • Mentor engineers and raise the technical bar for ML development, experimentation rigor, responsible AI usage, and production-quality agentic systems across the organization. What we're looking for: • 7+ years of experience building and deploying large-scale ML systems in production (e.g., ads ranking, recommendation, Agentic AI, or search), with strong end-to-end ownership from problem scoping through evaluation and experimentation, and solid software engineering skills in at least one modern language (e.g., Python, Java) and large-scale data systems. • Degree in Computer Science, Mathematics, or a related technical field, or equivalent experience. • Strong end-to-end ML ownership, including problem scoping, data and label design, feature engineering, model training, production deployment, offline/online evaluation, experimentation, and monitoring. • Deep understanding of recommendation system architectures such as candidate generation, retrieval, ranking, re-ranking, embeddings, vector search, multi-task learning, calibration, contextual bandits, or reinforcement learning. • Proven Staff-level technical leadership as a hands-on IC, setting technical direction and driving multi-quarter ML and systems roadmaps, including aligning stakeholders on priorities, trade-offs, and execution plans. • Excellent cross-functional communication and collaboration skills, building strong partnerships with product, data science, infra, and partner ML teams to clarify ambiguous problem spaces, co-create solutions, and drive consensus with senio