The Conversion Visibility Modeling team enables a performant ads marketplace and helps prove value to advertisers by connecting Pinterest onsite activity with conversions that happen offsite (both digital and physical) in a privacy-preserving way. As a Machine Learning Engineering Manager on this team, you will lead a hybrid team of ML engineers and backend software engineers to build end-to-end identity and conversion visibility solutions across modeling, serving, and data infrastructure, so advertisers retain accurate, privacy-aware performance visibility as signals fragment and degrade. You will set the technical direction for high-impact ML systems that feed ranking, bidding, measurement, and reporting across Pinterest’s ads stack.

What you’ll do: 

• Attract, hire, develop, and lead a hybrid team of ML engineers and backend software engineers, fostering strong collaboration across modeling and infrastructure and building an inclusive, high-performing environment where the team can deliver end-to-end solutions. 
• Lead a team responsible for the strategy, execution, and operational excellence of identity and conversion signal modeling systems (e.g., user match prediction, conversion type/value prediction, probabilistic attribution and deduplication) that improve match precision/recall and downstream conversion quality across web and app surfaces.
• Partner closely with product managers, data scientists, and tech leads to shape problem definitions, translate business needs into technical strategy, and drive execution toward high-impact outcomes.
• Collaborate closely with Ads Ranking & Bidding, Measurement Products, and Conversion Ingestion & Attribution teams to define interfaces, SLAs, and success metrics that enable end-to-end identity and conversion visibility systems—including models, data pipelines, and serving surfaces—to integrate cleanly into the broader ads ecosystem.
• Establish engineering best practices across both ML and backend development, including data quality, feature and data pipelines, model evaluation, experimentation, service reliability, and operational excellence, so the team can build trustworthy ML-powered systems end to end.
• Use AI to accelerate analysis and iteration on model ideas and architectures, while applying strong judgment, testing, and verification to ensure correctness, reliability, and advertiser trust.

What we’re looking for:

• 7+ years of experience building and deploying large-scale ML systems in production (e.g., ads, measurement, recommendation, ranking, or search).
• 2+ years of experience as an engineering manager or technical lead.
• Bachelors Degree in Computer Science, Statistics, or a related technical field, or equivalent experience.
• Nice to have: Meaningful hands-on experience or strong familiarity with ads conversion attribution, identity matching, ads ranking or ads measurement domains.
• Proven technical leadership across both ML and software systems, with experience setting direction for multi-quarter roadmaps that span modeling, data pipelines, backend services, and productionization, and 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.
  
  

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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