Senior Machine Learning Platform Engineer

Klaviyo•Boston, MA

About The Position

Klaviyo is building AI and machine learning technologies to power Composer and personalization products, including smart send time, audience optimization, and product recommendations. The Senior Machine Learning Platform Engineer will build and evolve the core platforms, tools, and infrastructure that ML and AI engineers use to create models and agents for Klaviyo’s products. The role focuses on training and deploying models, standardizing feature data, and scaling reliable systems for Klaviyo’s data and traffic volumes. This is a backend-heavy, hands-on technical individual contributor role based in Boston. The engineer will lead technical execution, write code for critical system paths, conduct code and design reviews, mentor engineers, and collaborate with product, ML, data science, infrastructure, and security partners. The role also contributes to platform architecture, technical direction, and roadmap planning.

Requirements

At least 7 years of experience as a software engineer, ML platform engineer, or in a similar role building backend or distributed systems.
Demonstrated experience designing and launching AI/ML systems in production at scale.
Strong proficiency in Python or a similar backend language such as Go, Java, or Scala.
Experience with cloud infrastructure and platform fundamentals, including AWS and Kubernetes.
Familiarity with data-intensive systems, including streaming or batch processing, data modeling, and designing for large-scale data.
Strong communication skills and a track record of driving technical excellence across teams.
Comfort collaborating with cross-functional technical partners, including ML, product, infrastructure, and security teams.

Responsibilities

Enable AI and ML teams to ship models and ML-powered features faster, more safely, and more reliably.
Evolve ML and LLM platform architecture so teams can build, evaluate, and operate production ML systems using consistent patterns and best practices.
Improve the reliability, observability, and cost efficiency of ML training, inference, and other ML workloads.
Architect and scale systems for feature storage and real-time retrieval for training and inference datasets.
Design workflows with AI and ML application teams for continuous model training, inference, and monitoring.
Lead technical execution, conduct code and design reviews, and author high-impact code for critical system paths.
Mentor engineers and promote engineering excellence, best practices, and strong design standards.
Drive architectural design and technical direction for core ML and Feature platform components.
Partner with product and engineering leaders to define the ML Platform roadmap.
Take accountability for delivery, reliability, and operational health of key ML Platform systems.
Participate in on-call and incident management for the team’s services.
Contribute to broader AI/ML strategy and platform direction.