Staff Software Engineer, Capacity Engineering

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 .

Pinterest is seeking a Staff Software Engineer, Capacity Engineering. The team is responsible for efficiently managing one of the largest-scale cloud-native infrastructures in the world. This role is highly impactful, as efficiency is an ongoing strategic priority for Pinterest. The role has direct visibility across Pinterest Engineering and with Engineering and company leadership. The team is looking for a candidate with a strong background in implementing performance and efficiency projects on large scale distributed systems.

Improve the efficiency of large scale shared environments like Kubernetes

Improve the performance and efficiency of large scale distributed systems that drive Pinterest systems

Build develop and mature profiling and optimization capabilities for Pinterest scale

Collaborate with Infrastructure Engineering and SRE teams in their mission to deliver highly available, resilient, secure and efficient foundations for Pinterest’s tech stack

Leverage AI to scale the impact of yourself and the team, including: Accelerate performance investigations (e.g. quickly distill logs/metrics/traces and prior learnings) while verifying findings through measurement and testing Build tooling and agents that allow users to self-serve efficiency insights and recommendations Iterate faster on optimization approaches and rollout plans, then validate impact with experiments and production guardrails

Accelerate performance investigations (e.g. quickly distill logs/metrics/traces and prior learnings) while verifying findings through measurement and testing

Build tooling and agents that allow users to self-serve efficiency insights and recommendations

Iterate faster on optimization approaches and rollout plans, then validate impact with experiments and production guardrails

Bachelor’s degree in computer science, a related field or equivalent experience

Deep understanding of infrastructure capacity and performance

Experience leading efficiency initiatives at scale on Kubernetes or other large scale shared infrastructure

Strong technical and performance engineering skills to collaborate with stakeholders on complex and ambiguous technical challenges

Experience building and managing highly available distributed applications at scale

Proficiency in software development languages such as Java, Python and C++

Excellent skills in communicating complex technical issues

Experience with AWS or similar cloud environments

Demonstrated ability to use AI to improve speed and quality in your day-to-day workflow for relevant outputs

Strong track record of critical evaluation and verification of AI-assisted work (e.g., testing, source-checking, data validation, peer review)

High integrity and ownership: you protect sensitive data, avoid over-reliance on AI, and remain accountable for final decisions and deliverables

Bonus points for the following: Hands-on experience with large, cloud-native multi-tenant platforms at Internet scale

Hands-on experience with large, cloud-native multi-tenant platforms at Internet scale