Klaviyo is looking to build machine learning-powered systems that decide which products to show to whom and when across their platform, to drive revenue for merchants of all sizes.
Requirements
- 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 day-to-day development.
- Comfortable with data-driven decision making and A/B testing—you understand how to instrument experiments, read results, and fold learnings back into the system.
- Familiarity with modern DevOps practices (CI/CD, monitoring, alerting) and how they apply to large-scale data and recommendation systems.
Responsibilities
- 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 productionize 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.
Other
- Excellent collaborator and communicator: you can explain tradeoffs to technical and non-technical partners and work effectively with ML Engineers, Software Engineers, PMs, and other teams.
- Proven track record of owning projects end-to-end—from design and implementation through rollout, monitoring, and iteration—ideally across multiple components or services.
- You’ve already experimented with AI in work or personal projects, and you’re excited to dive in and learn fast.
- Background in e-commerce, marketing tech, or consumer personalization products
- Bachelor's degree or higher in Computer Science or related field
