Lead Engineer, Applied AI (Customer Agent)

Klaviyo — Boston, United States

At a glance

Klaviyo is hiring for this senior on-site role based in Boston, United States. The advertised salary is $176k–$264k a year. It was posted on 8 October 2026 and is open until 7 November 2026.

  • Work model: On-site
  • Location: Boston, United States
  • Salary: $176k–$264k a year
  • Seniority: Senior (6+ years)
  • Category: Data Science
  • Posted: 8 October 2026
  • Open until: 7 November 2026

Tags: Software Engineer, full-time, Actively Hiring, B2b, Company Stage, Top Investors

Job description

Lead Engineer, Applied AI (Customer Agent)

About the Team:

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: we're building a conversational AI system that lets brands serve their own customers at a scale no human support team could match. We're still small enough that one engineer’s work visibly shapes how the whole platform evolves. We're 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.

About the Role:

As Lead Engineer, you'll set the technical and product direction for a core area of 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 lead role, with a wide footprint across our agentic product - from orchestration to governance - that grows based on where the product is when you join. We're looking for an engineer who owns complex, ambiguous problems, who has built, scaled, and supported a production agentic system through everything that comes after the demo, and who up-levels the people, process, and technology around them.

How You'll Make an Impact:

  • Set the technical and product direction for a core area of Customer Agent, from architecture through production, and contribute to the direction of the platform overall.
  • Take on the hardest technical problems in your area: orchestration, evaluation, guardrails, memory & context, failure handling for non-deterministic systems.
  • Write and ship code, review designs, and make the hard calls in your area when there’s no obvious right answer.
  • Define patterns that other engineers adopt, and mentor through design reviews rather than direct management.
  • Partner with product, design, data science, and go-to-market leaders to shape what gets built, not just how it’s built.
  • Evaluate emerging AI technologies and bring the ones worth betting on into production.

What We're Looking For:

  • You've built, scaled, and supported production agentic systems, not just prototyped them: real orchestration, evals, guardrails, and non-deterministic failure handling. You've owned your part of one through launch, incidents, and the operational reality after the demo.
  • You've 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.
  • You've owned technical and product direction for a significant area before, not just your own component, and you can point to something you designed and championed that shipped and that other engineers built on.
  • You're hands-on: writing code, reviewing your own and others’ architecture decisions, and debugging production issues yourself, not delegating the hardest problems away.
  • Your patterns and designs are reused by other engineers, and you've unblocked many others’ projects, not just your own.
  • You communicate complex technical trade-offs clearly across engineering and product audiences, and you can turn an undefined problem into a plan others rally behind.

Nice to Haves:

  • Experience working on 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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