Senior Machine Learning Platform Engineer

At Klaviyo, we believe the future of software lies not in productivity tools for human users but in software that can run and optimize itself based on outcome or reward metrics. We’ve built the infrastructure and application that serve as the interface between businesses and consumers. We now have over 167,000 customers, billions of consumer profiles, and hundreds of billions of customer messages and follow-on conversion data. We are building state-of-the-art AI and machine learning technologies at Klaviyo to power Composer, as well as personalization products such as smart send time, audience optimization and product recommendations.

As a Senior Machine Learning Platform Engineer, you will help build the core platforms, tools, and infrastructure that ML and AI engineers rely on to create advanced agents and models powering Klaviyo’s products. You will drive key technical initiatives for training and deploying models, unifying and standardizing feature data, and scaling systems that must operate reliably at Klaviyo’s data and traffic scale.

This is a backend-heavy, hands-on technical individual contributor role. You will lead technical execution, conduct code and design reviews, author high-impact code for critical system paths, and collaborate closely with product, ML, data science, and infrastructure partners to deliver durable, high-leverage platforms that accelerate AI development across Klaviyo.

This role is based in Boston.

How You’ll Make an Impact

• Enable AI / ML teams across Klaviyo to ship new models and ML-powered features faster, more safely, and more reliably.
• Evolve our ML and LLM platform architecture so teams can build, evaluate, and operate production ML systems with consistent patterns and best practices.
• Improve reliability, observability, and cost efficiency of ML training, inference, and other ML workloads that serve features like smart send time, audience optimization and product recommendations.
• Raise the bar on engineering and operational excellence for systems that support 167K+ customers and billions of events.
• Architect and scale systems supporting feature storage and real-time retrieval for training and inference datasets

What You’ll Do

• Work closely with AI & ML application teams to design seamless workflows 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 across the organization, promoting engineering excellence, best practices, and strong design standards.
• Drive architectural design and technical direction for core components of the ML and Feature platforms.
• Partner with product and engineering leaders to define the ML Platform roadmap.

Learn more about the team’s work in our blogs on Ray Jobs & Ray Tune.

Who You Are

We encourage candidates to apply even if they do not meet all the qualifications listed. ML Platform and MLOps are rapidly evolving spaces and we are all constantly learning.

• 7+ years of experience as a software engineer, ML platform engineer, or similar, 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 (AWS, kubernetes):
• Familiarity with data-intensive systems: streaming or batch processing, data modeling, and designing systems with large-scale data in mind (spark, ray-data).
• Comfortable collaborating with cross-functional technical partners (ML, product, infra, security).
• Strong communication skills and a track record of driving technical excellence across teams.

What to Expect in Your First 90 Days

30 Days

• Complete Klaviyo boot camp and onboarding for the AI & Analytics organization.
• Build strong context on our AI/ML stack, platform components, and roadmap.
• Meet your team, engineering peers, and key stakeholders across product, ML, infra, and security.
• Onboard to existing technical workstreams and gain familiarity with production ML deployment pipelines.

60 Days

• Deliver high-impact architectural enhancements or core features across the ML Platform end-to-end.
• Conduct deep technical dives into platform architecture and mentor peers through detailed code and design reviews.
• Partner with staff/principal engineers and product leaders to establish technical standards and design patterns for feature platform components.
• Partner with product and technical leaders to shape the next 2–3 quarters of roadmap at a high level.

90 Days

• Own accountability for delivery, reliability, and operational health of key ML Platform systems in your area.
• Have multiple features or platform capabilities shipped that are in active use by ML/AI product teams.
• Participate fully in on-call and incident management for your team’s services.
• Contribute to broader AI/ML strategy and platform direction, influencing how Klaviyo builds and operates models at scale.