Sr. Staff Software Engineer, Big Data Platform

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 senior staff software engineer to lead the next generation of data infrastructure at Pinterest which powers mission critical big data and AI applications. You’ll be working on some of the most exciting big data and AI open source technologies (Flink, Spark, Kubernetes, etc.), at the scale of exabytes of data to help Pinners discover and do what they love.

Lead the strategy and technical direction of Pinterest’s data infrastructure for big data and AI applications

Build and scale data infra frameworks and infrastructure to process petabytes-scale datasets, including compute engines, job management, resource management, scheduling and remote shuffling

Work with internal customers on critical business use cases that rely on big data

Provide thought leadership to the entire company on how data should be processed and stored more reliably, quickly and efficiently at scale