Staff Machine Learning Engineer, 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 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 Staff Machine Learning Engineer on this team you will be the founding ML IC driving identity and conversion signal modeling across our pipeline 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:

• Lead the design and implementation of identity and conversion signal models (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.
• Own one or more major identity prediction initiatives end-to-endfrom problem framing label and feature design and offline evaluation through production deployment and online experimentation.
• Build and evolve ML-powered components in the conversion visibility pipeline partnering with infra teams to create scalable low-latency systems for ingesting enriching and exposing conversion signals to ranking bidding measurement and reporting stacks.
• Establish ML development best practices (data quality feature pipelines evaluation experimentation) within Conversion Visibility and mentor engineers so non-ML partners can confidently contribute to ML-powered components.
• Collaborate closely with Ads Ranking & Bidding Measurement Products and Conversion Ingestion & Attribution teams to define interfaces SLAs and success metrics that ensure identity and signal models plug cleanly into the broader ads ecosystem.
• 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.
• Apply LLM-powered tools to synthesize experiment results technical docs and partner feedback into clear options and recommendations helping the team explore more approaches and converge on high-impact solutions faster.
  
  

What were looking for:

• Experience building and deploying large-scale ML systems in production (e.g. ads measurement recommendation ranking 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 machine learning statistics or related field
• Meaningful hands-on experience or strong familiarity with ads conversion identity/user matching or measurement domains ideally under privacy and signal-loss constraints (e.g. cookies IP ATT SKAN).
• Expertise in probabilistic modeling and measurement (e.g. identity prediction cohort-to-user inference modeled conversions data driven attribution) and in designing trustworthy metrics under noisy or partial labels.
• 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 once per week and therefore needs to be in a commutable distance from one of the following offices: Seattle San Francisco Palo Alto.

#LI-HYBRID

#LI-SM4