Software Engineer II - Recommendations

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. Software Engineer II - Recommendations (Boston, MA onsite 5x a week) Why you should join the Recommendations Platform Team The Recommendations Platform Team is responsible for developing and deploying machine learning-based recommendation systems at scale, and building out the foundation for new use cases for technologies such as embedding-based similarity search to power agentic workflows. We are evolving to act as a layer of product intelligence, making sense of customer data in order to personalize messages with the right item at the right time across multiple channels. Our systems span large-scale data pipelines and querying workflows, batch training and inference, and low-latency online retrieval and ranking systems. We are also building the experimentation, tracking, and measurement capabilities needed to evaluate recommendation quality and business impact over time. How you will make a difference Contribute to the architecture and evolution of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), meeting standards for reliability, performance, and clear APIs. Contribute to and maintain robust, large-scale data processing pipelines (e.g., using Apache Spark or similar frameworks) that transform raw events and catalog data into high-quality features and inputs for recommendation models, ensuring data quality and lineage. Collaborate closely with ML engineers and product stakeholders to productionize recommendation models —defining high-level interfaces, feature contracts, and deployment patterns for batch and/or real-time inference systems. Contribute to the development of the vector database that powers recommendation, semantic search, and agentic use cases. Ensure data and service observability (metrics, logging, tracing, dashboards) to facilitate recommendations that are correct, explainable, fast, and highly available for all customers. Work with Product to break down projects into clear milestones, balancing the need for rapid experimentation with technical soundness and long-term maintainability. Lead data-driven decision making and A/B testing efforts —ensuring recommendation systems are instrumented with the right metrics, and independently interpreting results to guide future product and engineering iterations. Participate in on-call and incident response for the systems you own, driving major post-incident follow-ups that substantially improve the resilience and operability of our recommendation stack. Integrate AI into your and the team’s development workflow from the ground up —for example, using AI to accelerate development, automate complex tests, or build smarter monitoring and debugging tools. Share knowledge, mentor junior engineers, and define best practices on working with large-scale data frameworks, distributed systems, and integrating ML into production systems.