Backend ML Engineer — Recommendations Platform

Klaviyo; Boston (MA); On-site; USD 116,000 - 174,000; Full time

Job summary

Klaviyo is seeking a Software Engineer II for the Recommendations Platform in Boston, onsite 5x a week. You will help architect backend services powering product recommendations across channels, build scalable data pipelines, and collaborate with ML engineers to productionize models.

You’ll own features end-to-end and contribute to observability, reliability, and deployment patterns. The role emphasizes Python proficiency, cloud-native architectures (AWS), and container orchestration

Qualifications

• 2+ years of professional software engineering experience with backend/distributed systems.
• Proficient in Python and open to other languages.
• Comfortable with cloud-native architectures (AWS preferred) and Kubernetes.
• Experience in data-driven decision making and A/B testing.
• Comfortable designing and querying data models in relational, analytical, and NoSQL stores.
• Familiar with modern DevOps practices and building scalable systems.
• Track record of owning features end-to-end from design to rollout.

Responsibilities

• Contribute to backend services powering product recommendations with reliable APIs.
• Build and maintain large-scale data pipelines transforming events and catalogs into features.
• Collaborate with ML engineers and product stakeholders to productionize models.
• Develop a vector database powering recommendation, search and agentic use cases.
• Ensure observability with metrics, logging and dashboards for reliability.

Skills

Python

Distributed systems

Data pipelines

A/B testing

Communication

Tools

Spark

Kubernetes

PostgreSQL

Redis

AWS

Job description

Klaviyo is seeking a Software Engineer II for the Recommendations Platform in Boston, onsite 5x a week. You will help architect backend services powering product recommendations across channels, build scalable data pipelines, and collaborate with ML engineers to productionize models.

You’ll own features end-to-end and contribute to observability, reliability, and deployment patterns. The role emphasizes Python proficiency, cloud-native architectures (AWS), and container orchestration