Senior Software Engineer, Agent Runtime & Customer Workflows

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.

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 167000 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.

Were looking for a Senior Software Engineer Agent Runtime & Customer Workflowsto join the Customer Agent team Klaviyos AI-native conversational platform. Youll 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.

Youll 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.

• 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.

• 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.

• 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.

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