Engineering Manager, Developer Agents

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

Engineering Manager for Pinterest's Agent Foundations team, responsible for architecting, scaling, and leading the development of foundational agent platform systems including serving, evaluation, and monitoring. The role involves technical leadership, team management, and partnering with product and research to define the future of agent experiences at Pinterest.

What you'd actually do

  1. Define the direction of Pinterest's agent platform and build critical components such as agent serving, evaluation, and monitoring.
  2. Partner with and advise internal product and engineering teams on agent tech stack needs to shape the future vision for the agent platform.
  3. Lead the team's technical direction and roadmap for key agent infrastructure and platform investments.
  4. Hire, mentor, and grow engineers across all levels, fostering a culture of technical excellence, ownership, and continuous learning.
  5. Use AI to accelerate team execution, system design, and decision-making, applying sound judgment to validate outputs and ensure correctness and quality.

Skills

Required

  • Python
  • designing, building, and operating scalable, highly available backend systems
  • production-grade infrastructure at scale
  • agent evaluation frameworks
  • prompt iteration workflows
  • harness engineering
  • cloud infrastructure on AWS
  • Docker
  • Kubernetes
  • technical leadership
  • people management
  • setting team vision and long-term roadmap
  • mentoring and growing engineers
  • driving day-to-day execution
  • engineering alignment
  • cross-functional partnership
  • AI to accelerate team execution, system design, and decision-making
  • critical evaluation and verification of AI-assisted work
  • integrity and ownership

Nice to have

  • agent serving
  • agent monitoring
  • agent tech stack needs

What the JD emphasized

  • hands-on background in Python
  • designing, building, and operating scalable, highly available backend systems
  • agent evaluation frameworks
  • prompt iteration workflows
  • harness engineering
  • cloud infrastructure on AWS
  • containerized services using Docker and Kubernetes
  • technical leadership and people management experience
  • setting team vision and long-term roadmap
  • mentoring and growing engineers
  • driving day-to-day execution and engineering alignment
  • partnering cross-functionally
  • use AI to accelerate team execution, system design, and decision-making
  • sound judgment in validating outputs
  • maintaining quality
  • taking ownership of final outcomes
  • critical evaluation and verification of AI-assisted work
  • testing
  • source-checking
  • data validation
  • peer review
  • High integrity and ownership
  • protect sensitive data
  • avoid over-reliance on AI
  • remain accountable for final decisions and deliverables

Other signals

  • building foundational platforms for agents
  • scaling agent experiences
  • agent serving and evaluation
  • technical leadership for agent platform