Lead People Systems Engineer

Klaviyo Klaviyo · Enterprise · Boston, MA · IT & Security

Lead People Systems Engineer to build AI-powered workflows, copilots, and agents for HR processes, defining the architecture, standards, and vision for AI integration within the employee lifecycle at an AI-first company. This role requires integrating with enterprise SaaS systems and ensuring responsible AI practices.

What you'd actually do

  1. Reimagine core People workflows (recruiting, onboarding, performance, daily tasks) through an AI-first lens, not just incremental automation
  2. Design and build AI-powered workflows, copilots, and agents that automate and augment People processes across the employee lifecycle
  3. Architect and implement systems that integrate across SaaS such as Workday, Greenhouse, Sana, and internal Klaviyo platforms
  4. Partner with our internal IT AI team and establish best practices for: Enable AI Use Cases (copilots, assistants, automated workflows) that drive faster execution and better decision-making for People teams
  5. Collaborate with stakeholders to translate business problems into scalable AI solutions

Skills

Required

  • 7+ years of engineering or data engineering experience
  • 2+ years building AI/ML or LLM-powered delivering tangible business outcomes
  • Proven ability to set technical direction and build 0→1 systems with meaningful organizational impact
  • Experience integrating with enterprise SaaS systems (HRIS, ATS, etc.)
  • Experience designing scalable systems, automation frameworks, and internal tools
  • Experience working with sensitive data and implementing responsible AI practices
  • Strong ability to translate ambiguous business problems into scalable technical solutions

Nice to have

  • Experience with People/HR systems (Workday, Greenhouse, Sana)
  • Familiarity with workflow automation platforms (e.g., n8n, Workato, Zapier)
  • Experience in high-growth SaaS environments
  • Contributions to AI governance, internal AI platforms, or experimentation frameworks

What the JD emphasized

  • building AI/ML or LLM-powered delivering tangible business outcomes
  • set technical direction
  • build 0→1 systems
  • working with sensitive data
  • implementing responsible AI practices

Other signals

  • AI-first company
  • design and build AI-powered workflows, copilots, and agents
  • define how AI becomes embedded into the employee lifecycle
  • build 0→1 systems