About this role
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 op