Staff Machine Learning Engineer, Ads Measurement Products

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, 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 . We’re looking for a Staff Machine Learning Engineer to lead the development of ML systems that power Pinterest’s first- and third-party ads measurement products. In this role, you’ll set the technical direction for scalable, trustworthy, and privacy-aware ML solutions that help advertisers understand the impact of their investment on Pinterest. You’ll work across Product, Engineering, Data Science, and external partners to turn rigorous measurement methods into production systems that improve measurement quality, efficiency, and decision-making. What you’ll do Lead the design, implementation, and productionization of ML-powered components for ads measurement products, including areas such as measurement methodologies, diagnostics, anomaly detection, automated insight generation, and advertiser decision-support. Build and evolve scalable ML and data pipelines that support first- and third-party measurement products, partnering with infrastructure and product engineering teams to create reliable, maintainable, and performant systems. Partner closely with Data Science to translate causal inference, incrementality, and experimentation methodologies into production-grade systems and tools that increase the speed, scale, and usability of measurement products without compromising rigor. Collaborate with internal and external measurement partners, such as clean rooms, conversion APIs, MMM partners, and MTA vendors, to integrate high-quality signals and develop joint measurement solutions. Establish ML engineering best practices across data quality, feature pipelines, evaluation, experimentation, monitoring, and model governance within Measurement Products, and mentor engineers and partner teams working on ML-powered components. Influence the Ads Product and Engineering roadmap by identifying high-leverage opportunities to apply ML to measurement workflows and products, and by driving clear technical trade-offs, interfaces, and success metrics across teams. Use AI to accelerate development, prototyping, analysis, and iteration, while applying strong judgment, testing, and verification to ensure correctness, explainability, data protection, and advertiser trust. What we’re looking for 7+ years of experience building and deploying large-scale ML systems in production, ideally in ads, measurement, recommendation, ranking, search, or closely related domains. Degree in Computer Science, Statistics, Engineering, or a related technical field, or equivalent experience. Meaningful hands-on experience in ads measurement, ad effectiveness, or incrementality domains, such as conversion lift, brand lift, budget-split testing, matched-market tests, MMM, MTA, conversion APIs, or clean-room-based measurement. Strong end-to-end ML ownership as an individual contributor, including scoping ambiguous problems, designing labels and features, building training and inference workflows, and defining robust offline and online evaluation strategies. Solid software engineering skills in at least one modern programming language such as Python or Java, plus strong experience with SQL and large-scale data systems. Expertise in probabilistic modeling, experimentation, and measurement under noisy or partial labels, with the ability to design trustworthy metrics and make principled trade-offs across precision, recall, bias, power, coverage, and data quality. Proven Staff-level technical leadership as a hands-on IC, including setting technical direction, driving multi-quarter roadmaps, and aligning senior stakeholders across Product, Engineering, Data Science, Infra, and partner teams. Demonstrated experience using AI coding assistants and LLM-powered productivity tools to improve speed and quality in development, experimentation, and data exploration, with a clear approach to val