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 you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to see how we empower creators to own their own destiny.

Senior AI Engineer, Customer Agent

About the Role

At Klaviyo, we believe the future of software lies not only in tools that help people work more efficiently, but in intelligent systems that can take action, learn from outcomes, and improve customer experiences over time.

Klaviyo serves more than 167,000 customers and processes billions of consumer profiles, messages, interactions, and conversion signals. This creates a unique opportunity to build state-of-the-art AI systems that help businesses create and execute better customer experiences at scale.

We’re looking for a Senior AI Engineer to join the Customer Agent team, Klaviyo’s AI-native conversational platform. You’ll design and build scalable backend systems and AI-powered product experiences that enable agents to reason, retrieve context, use tools, and take reliable action on behalf of customers.

You’ll be expected to independently own meaningful technical problems, make pragmatic engineering tradeoffs, and help evolve the architecture and quality of the systems you work on.

How You Will Make a Difference

• Design and build reliable, scalable backend systems and APIs that power AI-driven customer experiences.
• Build and evolve production agentic systems involving tool use, orchestration, context management, retrieval, structured outputs, and multi-step workflows.
• Solve hard applied AI problems across retrieval and RAG, grounding, model selection, hallucination mitigation, and deciding when deterministic or hybrid approaches are better than LLMs.
• Develop evaluation strategies using representative datasets, automated and human evaluation, regression testing, qualitative error analysis, and production signals.
• Improve production reliability through guardrails, fallbacks, retries, observability, monitoring, and thoughtful failure handling.
• Build asynchronous and distributed processing workflows that support high-volume AI workloads.
• Participate in an on-call rotation and help diagnose, mitigate, and learn from production incidents.