Lead People Systems Engineer

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

Lead People Systems Engineer to be the first dedicated AI engineer on the People Technology team, responsible for designing and building AI-powered workflows, copilots, and agents to transform how People teams operate within an AI-first company. This role involves architecting systems, establishing best practices for AI use cases, and ensuring responsible AI deployment, with a focus on integrating AI into the employee lifecycle.

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:
  5. Enable AI Use Cases (copilots, assistants, automated workflows) that drive faster execution and better decision-making for People teams

Skills

Required

  • 5+ 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

  • first dedicated AI engineer
  • design and build 0→1 systems
  • building AI/ML or LLM-powered delivering tangible business outcomes
  • Responsible AI, Privacy & Governance
  • sensitive employee data

Other signals

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