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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 senior stakeholders.
• Experience using AI coding assistants (e.g., Cursor, Claude Code) and LLM-powered productivity tools to accelerate development, experimentation, and data exploration, with a clear approach to validation, data protection, and critical review of AI-assisted work.
  

Relocation Statement:

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

In-Office Requirement Statement:

•  We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
• This role will need to be in the office for in-person collaboration 1 day per week and therefore needs to be in a commutable distance from one of the following offices [Seattle or Bay Area].

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