Manager II, Machine Learning – Conversion Visibility

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 were 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 others unique experiences and embrace theflexibility to do your best work. Creating a career you love Its Possible.

At Pinterest AI isnt just a feature its a powerful partner that augments our creativity and amplifies our impact and were looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities well 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 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 Pinterests ads stack.

What youll 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 systemsincluding models data pipelines and serving surfacesto 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 were 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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