Manager, Machine Learning & AI

About the Role

Zapier is looking for a Manager, Machine Learning & AI to lead our AI Platform team within the Data organization. AI is at the core of how Zapier helps customers automate work—powering everything from established AI/ML‑driven experiences to new experiments that push what’s possible with LLMs and agents.

The AI Platform team builds the shared foundations that make it faster and safer for product teams to ship AI/ML‑powered experiences: model runtime and serving patterns, LLMOps/MLOps tooling, evaluation harnesses, feature and data access patterns, reliability and cost controls, and developer workflows. You’ll lead a team of ML/AI engineers focused on enablement at scale, partnering closely with applied ML teams and product engineering to turn cross‑cutting needs into reusable primitives, platforms, and golden paths—so Zapier can ship AI features quickly without compromising safety, reliability, or cost.

About You

• 5+ years of experience in Machine Learning / AI and have shipped production systems end‑to‑end (design, launch, iteration, monitoring/on‑call).
• Experience leading engineers (people management or clear team‑lead responsibility); coach through feedback, delegation, and career development.
• Ability to translate ambiguous goals into a prioritized roadmap with milestones, measurable outcomes, and clear ownership.
• Built or operated platform/infrastructure/tooling used by other teams (internal platforms, ML platforms, data platforms, evaluation/experimentation platforms, or similar).
• Strong software engineering fundamentals (clean, testable code; CI/CD; operational readiness; reliability and incident response).
• Deep understanding of ML/LLM system design to guide decisions on serving patterns, evaluation/quality gates, observability, safe rollouts, and cost controls.
• Effective stakeholder management at scale: build trust across dozens of teams, align on clear interfaces and contracts, and drive broad adoption through documentation, enablement, and pragmatic support.
• Strong product judgment and comfort saying no: push back on low‑leverage requests, prioritize work that compounds across teams, and communicate trade‑offs clearly and respectfully.
• Balance speed, safety, reliability, and cost; default to shipping “works simply” solutions and iterating based on learnings.
• Clear communication with both technical and non‑technical audiences, making trade‑offs, progress, and impact easy to understand.
• Use AI in your work today—not occasionally, but as part of how you operate at a high level; can point to workflows you’ve built, how your approach has evolved through iteration, and the impact on quality, efficiency, and experience.