Principal Engineer, AI Platform

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

Principal Engineer on the AI Platform team at Pinterest, responsible for architecting the infrastructure for Generative AI and Recommender Systems. The role involves end-to-end engines for data orchestration, model training/fine-tuning, and high-performance inference, scaling to hundreds of millions of inferences per second for over 600 million monthly active users.

What you'd actually do

  1. Set the technical vision and roadmap for the Pinterest AI Platform, aligning with business priorities and long-term company strategy.
  2. Drive cross-functional strategic initiatives that advance our platform capabilities across:
  3. Cultivate a collaborative and inclusive culture where every team member feels welcome and empowered, and provide candid, constructive feedback that accelerates individual and team growth.

Skills

Required

  • C++
  • Java
  • Rust
  • AI/ML infrastructure
  • distributed systems
  • designing, building, and operating highly available, production-grade systems at large scale
  • technical judgment
  • company-wide impact
  • leading cross-organizational initiatives
  • navigating ambiguity
  • defining technical strategy
  • influencing architecture decisions
  • aligning cross-functional partners and executives
  • ownership
  • high engineering bar
  • engineering quality
  • engineering integrity
  • engineering accountability

Nice to have

  • Generative AI
  • Recommender Systems

What the JD emphasized

  • architect the infrastructure that powers both Generative AI and Recommender Systems
  • petabyte-scale data orchestration, model training and fine-tuning, and high-performance inference
  • scale seamlessly to hundreds of millions of inferences per second
  • Deep expertise in AI/ML infrastructure and distributed systems
  • highly available, production-grade systems at large scale
  • Proven track record of company-wide impact
  • Experience leading cross-organizational initiatives, navigating ambiguity, defining technical strategy, influencing architecture decisions, and aligning cross-functional partners and executives around a shared direction.
  • Strong ownership and a high engineering bar
  • Sets and upholds high standards for engineering quality, integrity, and accountability, and takes end-to-end ownership of outcomes.

Other signals

  • architect the infrastructure that powers both Generative AI and Recommender Systems
  • petabyte-scale data orchestration, model training and fine-tuning, and high-performance inference
  • scale seamlessly to hundreds of millions of inferences per second