Senior Software Engineer

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. Senior Software Engineer, Recommendations Boston, MA (Hybrid) What you'll do As the Senior Software Engineer for Product Recommendations, you will be a key contributor in building the machine learning–powered systems that decide which products to show to whom and when across all channels powered by our platform. This hands-on backend role focuses on converting billions of behavioral events into personalized product recommendations that drive revenue for merchants. You will define technical direction, build, and operate services and data pipelines end to end, from data ingestion and feature generation to ranking models and APIs. Lead the design, architecture, and operation of backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), upholding standards for reliability, performance, and clear APIs. Architect 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 strategically productionize recommendation models —defining high-level interfaces, robust feature contracts, and advanced deployment patterns for batch and/or real-time inference systems. Drive the development of ML/AI systems such as vector search that power recommendation, semantic search, and sophisticated agentic use cases. Implement and evolve data and service observability (metrics, logging, tracing, dashboards) to proactively ensure recommendations are correct, fast, and highly available for all customers. Contribute to and mentor others on shared data frameworks, libraries, and architectural patterns to accelerate the development of new recommendation use cases and iteration velocity across the team. 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. Champion and drive the transformation of engineering workflows by integrating AI 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/mid-level engineers, and define best practices on working with large-scale data frameworks, distributed systems, and integrating ML into production systems. Who you are 5+ years of software engineering experience, with experience building and operating mission-critical backend services and systems in a production environment. Experience in backend and distributed systems at scale; you have a proven track record working on high-throughput, highly available services and are an skilled in optimizing for latency, reliability, and operability. Proficient in Python and open to working in other languages Comfortable with cloud-native architectures (AWS preferred) and container orchestration ... (truncated, view full listing at source)