Staff Software Engineer, Observability

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 seeking an exceptional Staff Software Engineer to join our Observability team at Pinterest. This role combines deep technical expertise in distributed systems and data engineering with a product-oriented mindset to build world-class observability solutions that empower our engineering organization. As a Staff Engineer on the Observability team, you'll be responsible for designing and building the infrastructure and tools that provide visibility into Pinterest's large-scale distributed systems, helping thousands of engineers understand, debug, and optimize their services.

What You'Ll Do

: Define and execute the observability roadmap, treating it as a product.

What We’Re Looking For

: Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience Product Mindset: Demonstrated ability to work backwards from customer needs —understanding user needs, prioritizing features, measuring success, and iterating based on feedback. ), and data modeling at scale Observability Domain Knowledge: Hands-on experience with modern observability tools and practices including metrics, logging, tracing, and profiling.