Software Engineer II - Recommendations New Boston, MA

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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.

• 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 product ionize 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.

• 2+ years of professional software engineering experience with a focus on backend and distributed systems at scale; you have a proven track record working on production services and optimizing for latency, reliability, and operability as well as business requirements.
• Proficient in Python and open to working in other languages
• Comfortable with cloud‑native architectures (AWS preferred) and container orchestration (e.g., Kubernetes); you manage infrastructure and CI/CD pipelines as a core part of your development process.
• Experience in data‑driven decision making and A/B testing
  —you can define (or are interested in learning how to) how to instrument experiments, read and interpret results, and ensure learnings are folded back into system design.
• Comfortable…