Software Engineer- Product Recommendations

Software Engineer
- Product 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.

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As the Software Engineer, Product Recommendations at Klaviyo, you’ll help build the machine learning‑powered systems that decide which products to show to whom and when across our platform. You’ll work on large-scale backend and data systems that turn billions of behavioral events into real‑time, personalized product recommendations that drive revenue for merchants of all sizes.

You’ll join the Product Recommendation team, partnering closely with Machine Learning Engineers, AI Engineers, other engineers, Product Managers and Designers to design, build, and operate services and data pipelines that power our recommendation features end to end—from data ingestion and feature generation to ranking models and APIs exposed in product. This is a hands‑on backend role with a strong focus on building scalable systems and data processing frameworks, with prior ML system experience as a plus (not a hard requirement).

• Design, build, and operate backend services that power product recommendations across Klaviyo experiences (email, SMS, KAgent, onsite, etc.), with a focus on reliability, performance, and clear APIs.
• Build and maintain 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.
• Collaborate with ML engineers to product ionize recommendation models—defining interfaces, feature contracts, and deployment patterns for batch and/or real‑time inference.
• Build ML/AI systems such as vector search that power recommendation, semantic search, and agentic use cases.
• Implement and evolve data and service observability (metrics, logging, tracing, dashboards) to ensure recommendations are correct, fast, and available when customers need them.
• Contribute to and improve shared data frameworks, libraries, and patterns that make it easier to build new recommendation use cases and iterate quickly.
• Work with product managers to break down complex recommendation initiatives into clear milestones, helping balance experimentation speed with reliability and technical soundness.
• Partner on data‑driven decision making and A/B testing—ensuring recommendation systems are instrumented with the right metrics, and helping interpret results to guide future iterations.
• Participate in on‑call and incident response for the systems you own, driving follow‑ups that improve the resilience and operability of our recommendation stack.
• Transform workflows by putting AI at the center, building smarter systems and ways of working from the ground up—for example, using AI to accelerate development, automate tests, or better monitor and debug recommendation behavior.
• Share knowledge and mentor other engineers on working with large‑scale data frameworks, distributed systems, and best practices for integrating ML into production systems.

• 3+ years of software engineering experience, including building and operating backend services in production.
• Strong focus on backend and distributed systems at scale; you’ve worked on high‑throughput or highly available services and care about latency, reliability, and operability.
• Proficient in Python, and comfortable working in at least one modern language used for backend/data work (e.g., Java or Scala).
• Proficient with big data frameworks such as Apache Spark (or similar technologies like Flink, Beam, etc.) for building batch or streaming pipelines.
• Comfortable with cloud‑native architectures (AWS preferred) and container orchestration (e.g., Kubernetes); able to work with infrastructure and CI/CD pipelines as part of your…