Staff Machine Learning Engineer, Conversion Visibility

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, we’re 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 other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.

At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll 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 Pinterest’s ads stack.

• →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.
  
  

• 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.