Job description

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 you’re a close but not exact match with the description, we hope you’ll still consider applying. Want to learn more about life at Klaviyo? Visit klaviyo.com/careers to 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. 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. Massachusetts Applicants: It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Our salary range reflects the cost of labor across various U.S. geographic markets. The range displayed below reflects the minimum and maximum target salaries for the position across all our US locations. The base salary offered for this position is determined by several factors, including the applicant’s job-related skills, relevant experience, education or training, and work location. In addition to base salary, our total compensation package may include participation in the company’s annual cash bonus plan, variable compensation (OTE) for sales and customer success roles, equity, sign-on payments, and a comprehensive range of health, welfare, and wellbeing benefits based on eligibility. Your recruiter can provide more details about the specific salary/OTE range for your preferred location during the hiring process. Base Pay Range For US Locations: $148,000 — $222,000 USD This role may require up to 10% travel for purposes such as new hire onboarding, client or partner work if applicable, team meetings, and industry events. Travel is coordinated in advance. Get to Know Klaviyo We’re Klaviyo (pronounced clay-vee-oh). We empower creators to own their destiny by making first-party data accessible and actionable like never before. We see limitless potential for the technology we’re developing to nurture personalized experiences in ecommerce and beyond. To reach our goals, we need our own crew of remarkable creators—ambitious and collaborative teammates who stay focused on our north star: delighting our customers. If you’re ready to do the best work of your career, where you’ll be welcomed as your whole self from day one and supported with generous benefits, we hope you’ll join us. AI fluency at Klaviyo includes responsible use of AI (including privacy, security, bias awareness, and human-in-the-loop). We provide accommodations as needed. By participating in Klaviyo’s interview process, you acknowledge that you have read, understood, and will adhere to our Guidelines for using AI in the Klaviyo interview Process . For more information about how we process your personal data, see our Job Applicant Privacy Notice . Klaviyo is committed to a policy of equal opportunity and non-discrimination. We do not discriminate on the basis of race, ethnicity, citizenship, national origin, color, religion or religious creed, age, sex (including pregnancy), gender identity, sexual orientation, physical or mental disability, veteran or active military status, marital status, criminal record, genetics, retaliation, sexual harassment or any other characteristic protected by applicable law. IMPORTANT NOTICE: Our company takes the security and privacy of job applicants very seriously. We will never ask for payment, bank details, or personal financial information as part of the application process. All our legitimate job postings can be found on our official career site. Please be cautious of job offers that come from non-company email addresses (@klaviyo.com), instant messaging platforms, or unsolicited calls. By clicking "Submit Application" you consent to Klaviyo processing your Personal Data in accordance with our Job Applicant Privacy Notice. If you do not wish for Klaviyo to process your Personal Data, please do not submit an application. You can find our Job Applicant Privacy Notice here and here (FR).

Requirements

Must-haves

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 .
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
Complete Klaviyo boot camp and onboarding for the AI & Analytics organization.
Build strong context on our AI/ML stack, platform components, and roadmap.

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlllmpythongojavaawskubernetes

Platform: greenhouse
Effort: quick