Staff Software Engineer, Growth AI

Pinterest Pinterest · Consumer · San Francisco, CA · Core Engineering

Staff Software Engineer role focused on architecting and shipping AI-powered growth experiences at scale for Pinterest's Growth organization. This role involves building production systems, partnering with ML teams, defining service interfaces, and managing tradeoffs. It emphasizes an AI-native engineering model with eval-driven shipping and a prototype-first culture.

What you'd actually do

  1. 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.
  2. 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.
  3. Design and build the cross-team technical foundations that serve both teams: AI evaluation systems, content quality pipelines, agent skill platform, knowledge brain.
  4. 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.
  5. Mentor engineers across both teams as a peer Staff IC, deepening their technical work without managing them.

Skills

Required

  • 8+ years of product / systems engineering experience
  • Cross-team technical leadership
  • Working knowledge of common ML/AI techniques (supervised learning, embeddings, ranking, LLMs, evaluation methodology)
  • Strong track record partnering with ML/research teams to ship AI-powered features in production
  • Demonstrated AI-augmented engineering practice

Nice to have

  • Direct hands-on experience shipping applied ML, LLM/VLM, or agentic systems in production
  • Domain experience in search, discovery, recommendations, ranking, growth, or SEO
  • Experience as the product-engineering counterpart on an AI-first product launch from prototype to scale

What the JD emphasized

  • strong track record shipping production systems at scale that touch real users
  • architectural anchor on multi-team initiatives
  • strong track record partnering with ML/research teams to ship AI-powered features in production
  • AI-augmented engineering practice

Other signals

  • shipping AI-powered growth experiences at scale
  • architect and ship AI-powered growth experiences at scale
  • product engineering anchor
  • partnering closely with ML teams to take capabilities from prototype to production
  • AI in every part of the SDLC
  • eval-driven shipping