Sr. Lead Engineer, Applied AI (Customer Agent)

At Klaviyo we value the unique backgrounds experiences and perspectives each Klaviyo (we call ourselves Klaviyos) brings to our workplace each and every day. We believe everyone deserves a fair shot at success and appreciate the experiences each person brings beyond the traditional job requirements. If youre a close but not exact match with the description we hope youll still consider applying. Want to learn more about life at Klaviyo Visit see how we empower creators to own their own destiny.

Our Engineering team builds the AI systems that power how brands run their businesses on Klaviyo. We move fast ship in production and hold a high bar for what AI-native actually means: systems that make real judgment calls not demos.

The Customer Agent team is one of the newest and most ambitious groups in that org based in Boston and working in-office: were building a conversational AI system that lets brands serve their own customers at a scale no human support team could match. Were still small enough that a single senior engineer can shape how the whole platform evolves. Were past the demo stage: the team ships changes against live customer traffic measures quality with real evaluation frameworks and treats reliability as a product feature not an afterthought.

As Senior Lead Engineer youll set technical and product direction for Customer Agent end to end building a 0-to-1 agentic product with the speed and ownership of a startup backed by the scale and resources of one of the most innovative tech companies on the East Coast while staying hands-on with the hardest problems yourself. This is a principal-caliber individual contributor role: were looking for an engineer who has personally architected built scaled and supported a production agentic system through everything that comes after the demo and who acts as a force multiplier across the org through the strength of their technical judgment.

• Set the technical and product direction for Customer Agents core platform from architecture through production.
• Take on the platforms hardest technical problems personally: orchestration evaluation guardrails memory & context self-healing/improving and failure handling for non-deterministic systems.
• Write and ship code review designs and make the calls other engineers cant make alone.
• Build architecture and frameworks that other engineers and teams adopt and mentor through design reviews rather than direct management.
• Partner with product design data science and go-to-market leaders to shape the roadmap not just execute against it.
• Evaluate emerging AI technologies and bring the ones worth betting on into production.

• Youve architected built scaled and supported a production agentic system end to end not just prototyped one: real orchestration evals guardrails and non-deterministic failure handling. Youve owned it through launch scale incidents and the years of operational reality after the demo.
• Youve learned the hard lessons that only come from running agents in production: securing them against misuse and abuse and the operational scar tissue of keeping a non-deterministic system reliable as usage grows.
• Youve set technical and product direction for an entire product area before not just your own component and you can point to something you designed and championed that shipped and that other teams built on.
• Youve stayed hands-on through all of it: still writing code reviewing your own architecture decisions and debugging production issues yourself not delegating the hardest problems away.
• Your past architecture and frameworks got reused by other teams and youve unblocked multiple teams at once not just your own.
• You communicate complex technical trade-offs clearly across engineering product and executive audiences and you can turn an undefined problem into a plan others rally behind.

• Experience running a multi-tenant agent platform at production scale where external customers configure their own agent behavior on shared infrastructure.
• Experience building 0-to-1 products or working in startup environments.
• A track record with production evaluation systems AI observability or human-in-the-loop workflows for LLM-powered products.

Boston MA In-Office Role

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