Staff Machine Learning Engineer, Conversion Visibility

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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 Pinterest’s ads stack.

What you’ll 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-end-from 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 we’re 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.

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Pinterest is an equal opportunity employer and makes employment decisions on the basis of merit. We want to have the best qualified people in every job. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you require an accommodation during the job application process, please notify accessibility@pinterest.com for support.

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