Manager II, Machine Learning Engineering, Monetization

Within the Ads Performance and Ads Delivery teams, we try to connect the dots between the aspirations of Pinners and the products offered by our partners. Pinterest Machine Learning Engineers build personalized experiences to help Pinners create a life they love. With just over 4,000 global employees, our teams are small, mighty, and still growing. At Pinterest, you'll experience hands-on access to an incredible vault of data and contribute large-scale recommendation systems in ways you won't find anywhere else.

As a Manager, you will lead and empower a team of talented Machine Learning Engineers, collaborating closely with product managers, data scientists, designers, and engineers across the company to deliver impactful, pinner-facing ad products end-to-end.

We are currently hiring for multiple Manager II, Machine Learning Engineering roles across Ads Ranking, Ads Retrieval, and Ads Marketplace teams.

The scope of the work across these different areas could be leading the ads retrieval team in advancing Pinterest's global Shopping Ads platform through cutting-edge ML technologies and scalable infrastructure, driving impact across a 5B+ ads index; or leading the ads signals team in developing advanced ML signals and pre-training techniques to power Pinterest's ranking and retrieval models, and driving performance through innovative representation learning; or leading the ads marketplace team in building scalable product onboarding systems, applying state-of-art machine learning techniques to enhance ads product triggering, ranking, and selection, and driving value via the ads marketplace systems for bidding, budgeting, ad ranking, tuning, pricing, ad allocation and placement.

What you'll do:

• Lead and scale a high-impact team of Machine Learning Engineers by setting technical vision, developing a long-term roadmap, and fostering a strong team culture
• Design and deploy large-scale ML models to improve engagement prediction, ads retrieval, ranking efficiency, and/or overall marketplace performance
• Partner closely with cross-functional teams (including product, sales, data science, and engineering) to develop next-generation ad experiences that improve conversion, relevance, and value for both Pinners and advertisers
• Contribute to the evolution of our ads stack by advancing ranking models, retrieval candidate generation, signal development, and bidding/marketplace algorithms
• Drive end-to-end development of pinner-facing ad products, ensuring both system scalability and measurable business impact
• Uphold high standards for engineering excellence through strong code quality, scalable system design, and operational best practices
• Stay informed of emerging ML research and industry developments; evaluate and integrate emerging techniques to keep our systems state-of-the-art
• Recruit, mentor, and retain top ML talent while providing hands-on guidance, clear technical direction, and continuous professional development opportunities

What we're looking for:

• M.S. or Ph.D. in Computer Science, Machine Learning, or a related field
• 6+ years of industry experience in building production machine learning systems at scale, data mining, search, recommendations, with related background in recommender systems, ranking/retrieval or representation learning domains
• 1-2 years of experience as a TLM or EM managing engineering teams
• Excellent written and verbal communication skills and strong strategic thinking and cross-functional collaboration
• Strong software engineering and mathematical and analytical skills
• Hands-on experience with large-scale online ranking/recommendation systems.
• Experience with Ads domain is a plus.

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 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 once a week and therefore needs to be in a commutable distance from one of the following offices: San Francisco / Palo Alto / Seattle.

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