Engineering Manager, ML Platform

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 youre a close but not exact match with the description we hope youll still consider applying. Want to learn more about life at Klaviyo Visit see how we empower creators to own their own destiny.

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. Weve built the infrastructure and application that serve as the interface between businesses and consumers. We now have over 167000 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 marketing agent service agent and personalization products such as smart send time audience optimization and product recommendations.

As a Machine Learning Platform Engineering Manager you will lead a team of engineers building the core platforms tools and infrastructure that ML and AI engineers rely on to create advanced agents and models powering Klaviyos products. Your team will own platforms for training and deploying models and scaling systems that must operate reliably at Klaviyos data and traffic scale.

This is a backend-heavy hands-on technical leadership role. You will manage and grow engineers shape the technical roadmap for ML Platform 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 hybrid and based in Boston.

How Youll 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.
• Build and grow a high-performing inclusive engineering team that operates with ownership curiosity and strong customer focus.

What Youll Do

• Lead coach and grow a team of software / ML platform engineers (typically 610 direct reports).
• Set clear goals provide regular feedback and support career development aligned with Klaviyos Engineering Career Architecture.
• Stay hands-on with the technology: participate in design reviews code reviews and occasionally ship code for critical paths or prototypes.
• Partner with product and technical leaders to define and evolve the ML Platform strategy and roadmap balancing short-term delivery with long-term platform investments.

Learn more about the teams 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.
• 2 years of experience in a formal people leadership role
• Demonstrated experience launching and operating 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 clarity and alignment across teams.