Staff Software Engineer, Capacity Engineering

Pinterest is a platform that inspires creativity and innovation, and they are seeking a Staff Software Engineer for their Capacity Engineering team. This role focuses on enhancing the efficiency of large-scale cloud-native infrastructures and involves collaboration with various engineering teams to ensure high performance and reliability of Pinterest’s tech stack.

Responsibilities:

• 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

Requirements:

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