Pinterest is seeking a Staff Software Engineer for its Capacity Engineering team, which manages one of the largest-scale cloud-native infrastructures globally. This role focuses on driving performance and efficiency in large-scale distributed systems, particularly within Kubernetes environments. The position requires collaboration with various engineering teams and leadership to enhance system efficiency. Candidates should have a strong background in performance engineering and a passion for leveraging AI to improve workflows.

• Bachelor's degree in computer science, a related field or equivalent experience
• Deep understanding of infrastructure capacity and performance, with a strong command of system and hardware fundamentals
• Experience leading efficiency initiatives at scale on Kubernetes or other large scale shared infrastructure
• Strong technical and performance engineering skills, including fluency with profiling and performance tools
• 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
• Strong track record of critical evaluation and verification of AI-assisted work
• High integrity and ownership regarding sensitive data and accountability

• Drive efficiency in large-scale shared environments like Kubernetes, including scheduling optimization and resource-overhead reduction
• Improve the performance and efficiency of large scale distributed systems that drive Pinterest systems
• Use profiling and performance tooling fluently to root-cause performance and efficiency problems
• Collaborate with Infrastructure Engineering and SRE teams to deliver highly available and efficient foundations for Pinterest’s tech stack
• Leverage AI to scale the impact of yourself and the team, including accelerating performance investigations
• 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