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 f