Senior Software Engineer, Agent Runtime & Customer Workflows

Title: Senior Software Engineer, Agent Runtime & Customer Workflows Location: Boston, MA

Senior Software Engineer, Agent Runtime & Customer Workflows
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 Software Engineer, Agent Runtime & Customer Workflows 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.
• Work directly with external customers and cross-functional partners to understand workflows, identify pain points, and translate feedback into product and technical improvements.
• Monitor shipped experiences and use customer behavior, quality metrics, and system performance to guide iteration.

Who You Are

• You have 5-7 years of professional software engineering experience, with a strong foundation in backend systems and distributed applications.
• You have hands-on experience building and deploying generative AI or agentic AI applications into production for real users.
• You are proficient in Python and modern backend frameworks such as FastAPI or Django.
• You have experience with asynchronous processing and distributed task or event systems such as Celery, Kafka, SQS, RabbitMQ, or Redis.
• You have strong knowledge of databases, data modeling, APIs, and persistence patterns used in production systems.
• You are comfortable working in cloud-native environments with technologies such as AWS, containers, Kubernetes, infrastructure automation, or CI/CD systems.
• You can reason clearly about tradeoffs among quality, latency, cost, reliability, and implementation complexity.
• You are able to operate independently in ambiguous environments, break down loosely defined problems, and make pragmatic decisions about what to build first.
• You have experience with and genuine interest in working directly with external customers to understand problems and improve products.
• You care about shipping useful, reliable products and bring curiosity and sound engineering judgment to a rapidly evolving technical space.

Nice to Have

• Experience training, fine-tuning, distilling, or otherwise adapting machine learning or language models for production use.
• Experience with reinforcement learning or feedback-driven optimization.
• Experience building evaluation infrastructure such as LLM-as-judge systems, evaluator calibration frameworks, benchmark suites, or AI quality platforms.
• Experience operating AI systems at substantial scale, including optimizing inference cost, latency, or throughput.

#indeedRD