At Zapier, we build and use automation every day to make work more efficient, creative, and human. So if you’re using AI tools while applying here - that’s great! We just ask that you use them responsibly and transparently.
Check out our guidance on How to Collaborate with AI During Zapier’s Hiring Process, including how to use AI tools like ChatGPT, Claude, Gemini, or others during our hiring process - and when not to.
Location: NAMER / EMEA (Remote)
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. This is an exciting moment to join: we’re scaling proven AI capabilities while also building the next generation of AI features, which means there’s real impact today and meaningful greenfield work ahead.
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.
You have 5+ years of experience in Machine Learning / AI and have shipped production systems end-to-end (from design, launch, iteration, monitoring/on-call).
You have experience leading engineers (people management or clear team-lead responsibility), and you coach through feedback, delegation, and career development.
You can translate ambiguous goals into a prioritized roadmap with milestones, measurable outcomes, and clear ownership.
You have built or operated platform / infrastructure / tooling used by other teams (internal platforms, ML platforms, data platforms, evaluation/experimentation platforms, or similar).
You have strong software engineering fundamentals (clean, testable code; CI/CD; operational readiness; reliability and incident response).
You understand ML/LLM system design well enough to guide decisions on serving patterns, evaluation/quality gates, observability, safe rollouts, and cost controls.
You’re effective at stakeholder management at scale: you build trust across dozens of teams (not just a handful of close partners), align on clear interfaces and contracts, and drive broad adoption through documentation, enablement, and pragmatic support.
You have strong product judgment and are comfortable saying no: you push back on low-leverage requests, prioritize work that compounds across teams, and communicate tradeoffs clearly and respectfully.
You balance speed, safety, reliability, and cost, and you default to shipping “works simply” solutions and iterating based on learnings.
You communicate clearly with both technical and non-technical audiences, making tradeoffs, progress, and impact easy to understand.
Lead the AI Platform team: hire, coach, and develop ML/AI engineers; run team rituals; manage performance; and build a strong, inclusive team culture.
Deliver platform outcomes that help product teams ship AI faster and more safely: paved roads, reusable libraries/services, standard patterns, and clear developer documentation.
Own the AI Platform roadmap with your PM partner, translating cross-team needs into a prioritized plan with clear tradeoffs, measurable adoption, and business impact.
Partner cross-functionally with product teams, Applied AI/ML teams, Security, and Data Platform teams to ensure platform capabilities integrate cleanly and are widely adopted.
Drive operational excellence: define SLOs where appropriate, run incident reviews, improve alerting/monitoring, and ensure the platform is dependable and cost-effective.
Participate in AI vendor evaluation and management as needed (requirements, trials, integration plans, and cost/performance monitoring).
Communicate clearly and often: share updates, tradeoffs, and platform adoption progress; make the work visible; and keep stakeholders aligned.
The anticipated application window is 30 days from the date job is posted, unless the number of applicants requires it to close sooner or later, or if the position is filled.
Even though we’re an all-remote company, we still need to be thoughtful about where we have Zapiens working. Check out this resource for a list of countries where we currently cannot have Zapiens permanently working.