Software Engineer II - Recommendations at Klaviyo

<div class="content-intro"><p><em>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 <a class="_ymio1r31 _ypr0glyw _zcxs1o36 _mizu194a _1ah3dkaa _ra3xnqa1 _128mdkaa _1cvmnqa1 _4davt94y _4bfu18uv _1hms8stv _ajmmnqa1 _vchhusvi _kqswh2mm _ect4ttxp _syaz13af _1a3b18uv _4fpr8stv _5goinqa1 _f8pj13af _9oik18uv _1bnxglyw _jf4cnqa1 _30l313af _1nrm18uv _c2waglyw _1iohnqa1 _9h8h12zz _10531ra0 _1ien1ra0 _n0fx1ra0 _1vhv17z1" href="http://klaviyo.com/careers" data-renderer-mark="true">klaviyo.com/careers</a> to see how we empower creators to own their own destiny.</em></p></div><p><strong>Software Engineer II - Recommendations </strong></p> <p><strong>(Boston, MA onsite 5x a week)</strong></p> <p> </p> <p><strong>Why you should join the Recommendations Platform Team </strong></p> <p>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.</p> <p> </p> <p><strong>How you will make a difference </strong></p> <ul> <li><strong>Contribute to the architecture and evolution of backend services</strong> that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), meeting standards for reliability, performance, and clear APIs.</li> <li><strong>Contribute to and maintain robust, large-scale data processing pipelines</strong> (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.</li> <li><strong>Collaborate closely with ML engineers and product stakeholders to productionize recommendation models</strong>—defining high-level interfaces, feature contracts, and deployment patterns for batch and/or real-time inference systems.</li> <li><strong>Contribute to the development of the vector database</strong> that powers recommendation, semantic search, and agentic use cases.</li> <li><strong>Ensure data and service observability</strong> (metrics, logging, tracing, dashboards) to facilitate recommendations that are correct, explainable, fast, and highly available for all customers.</li> <li><strong>Work with Product to break down projects</strong> into clear milestones, balancing the need for rapid experimentation with technical soundness and long-term maintainability.</li> <li><strong>Lead data-driven decision making and A/B testing efforts</strong>—ensuring recommendation systems are instrumented with the right metrics, and independently interpreting results to guide future product and engineering iterations.</li> <li><strong>Participate in on-call and incident response for the systems you own,</strong> driving major post-incident follow-ups that substantially improve the resilience and operability of our recommendation stack.</li> <li><strong>Integrate AI into your and the team’s development workflow from the ground up</strong>—for example, using AI to accelerate development, automate complex tests, or build smarter monitoring and debugging tools.</li> <li><strong>Share knowledge, mentor junior engineers, and define best practices</strong> on working with large-scale data frameworks, distributed systems, and integrating ML into production systems.</li> </ul> <p><strong>Who you are</strong></p> <ul> <li><strong>2+ years of professional software engineering experience with a focus on backend and distributed systems at scale;&

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