AI Engineer II

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

This is a backend-heavy role with opportunities to contribute to user-facing experiences. You’ll independently own meaningful pieces of larger systems, contribute to technical decisions, and continue developing your depth across backend engineering, applied AI, and production reliability.

• Build and improve reliable backend systems and APIs that power AI-driven customer experiences.
• Develop production agentic features involving tool use, context management, retrieval, structured outputs, orchestration, and multi-step workflows.
• Contribute to solving applied AI problems across retrieval and RAG, grounding, hallucination mitigation, model selection, and choosing between LLM-based, deterministic, or hybrid approaches.
• Build and use evaluation approaches including representative datasets, automated and human evaluation, regression testing, qualitative error analysis, and production signals.
• Improve system reliability through guardrails, retries, fallbacks, observability, monitoring, and thoughtful failure handling.
• Build asynchronous and distributed processing workflows that support AI workloads at scale.
• Participate in an on-call rotation and help diagnose, mitigate, and learn from production incidents.
• Work with external customers and cross-functional partners to understand workflows, identify pain points, and translate feedback into product improvements.
• Monitor shipped experiences and use quality metrics, system performance, and customer feedback to guide iteration.

• You have 3+ years of professional software engineering experience, with experience building backend systems or distributed applications.
• You have hands-on experience building or contributing to generative AI or agentic AI applications, ideally used by real users or in production environments.
• You are proficient in Python and have experience with modern backend frameworks such as FastAPI or Django.
• You have experience with asynchronous processing or distributed task/event systems such as Celery, Kafka, SQS, RabbitMQ, or Redis.
• You have working knowledge of databases, data modeling, APIs, and persistence patterns used in production systems.
• You are comfortable working in cloud environments and have experience with technologies such as AWS, containers, Kubernetes, infrastructure automation, or CI/CD systems.
• You can reason about tradeoffs among quality, latency, cost, reliability, and implementation complexity, and know when to seek additional technical context.
• You are comfortable working through ambiguity, breaking larger problems into smaller pieces, and making progress without every requirement being fully specified.
• You have experience with, or strong interest in, working directly with external customers to understand problems and improve products.
• You care about shipping useful, reliable products and are motivated to deepen your engineering judgment in a rapidly evolving technical space.

• Experience training, fine-tuning, distilling, or otherwise adapting machine learning or language models.
• Experience with reinforcement learning or feedback-driven optimization.
• Experience with evaluation infrastructure such as LLM-as-judge systems, evaluator calibration, benchmark datasets, or AI quality tooling.
• Experience operating AI systems at meaningful production scale or optimizing inference cost, latency, or throughput.

#indeedRD

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