Staff Software Engineer, Pinterest

• 8+ years of product / systems engineering experience, with a strong track record shipping production systems at scale that touch real users
• Cross-team technical leadership: you've been the architectural anchor on multi-team initiatives, set direction others build on, and mentored through code
• Working knowledge of common ML/AI techniques (supervised learning, embeddings, ranking, LLMs, evaluation methodology) — enough to scope ML projects, reason about ML system design, and collaborate effectively with MLEs
• Strong track record partnering with ML/research teams to ship AI-powered features in production
• Demonstrated AI-augmented engineering practice — fluent use of Cursor / Claude Code / agentic tools, with a clear approach to validating AI-generated work
• Upsell, influence the scope of the team (e.g. cross team collaboration on potential signal sharing and other opportunities )
• Bachelor’s/Master’s degree in a relevant field such as Computer Science, or equivalent experience

Desirable

• We're hiring a Staff Software Engineer to be the product engineering anchor across two closely partnered teams: our SEO Product team, which owns Pinterest's durable organic discovery channels, and a new exploratory team focused on AI-first growth products
• This is a hands-on technical role for a strong product engineer who wants to architect and ship AI-powered growth experiences at scale, partnering closely with ML teams to take capabilities from prototype to production
• You'll set the technical bar through your own code and architecture — not by stepping into management
• Architect and build the production systems that AI/ML capabilities ship through across two Growth teams: APIs, data pipelines, content systems, evaluation and monitoring infrastructure
• Partner with Pinterest's centralized ML org and applied research teams to productize AI/ML capabilities at scale — defining service interfaces, building eval pipelines, and managing latency, cost, and quality tradeoffs
• Design and build the cross-team technical foundations that serve both teams: AI evaluation systems, content quality pipelines, agent skill platform, knowledge brain
• Set the technical bar across two teams operating an AI-native engineering model: AI in every part of the SDLC, eval-driven shipping, prototype-first culture
• Mentor engineers across both teams as a peer Staff IC, deepening their technical work without managing them