About the Role
At Klaviyo, we believe the future of software lies not in productivity tools for human users but in systems that can learn, run, and optimize themselves based on measurable outcomes. We’ve built the infrastructure and applications that power the interface between businesses and consumers — supporting over 182,000 customers, billions of consumer profiles, and hundreds of billions of customer interactions.
Now, we’re building the next generation of AI and machine learning technologies to power these products — enabling AI agents that can automatically create, execute, and optimize marketing and customer experience strategies for any business.
As a Lead AI Engineer, you’ll take ownership of designing, building, and scaling the backend and machine learning systems that form the foundation of Klaviyo’s AI strategy. You’ll partner closely with product managers, data scientists, and engineers to deliver intelligent, outcome-driven systems that operate at massive scale — while mentoring others and setting technical direction for the team.
How You Will Make a Difference
- Lead the design and development of backend and ML systems that scale to serve 182K+ customers.
- Build robust, reliable, and efficient data processing and model-serving pipelines that power production ML models.
- Develop and optimize predictive systems (e.g., conversion likelihood, engagement ranking, personalization, anomaly detection).
- Architect systems and services that enable our AI agents to learn and improve autonomously from reward or outcome signals.
- Collaborate cross-functionally to translate business challenges into scalable, measurable ML solutions.
- Collaborate with software engineers and product managers to turn predictive models into intuitive product features for our customers.
- Mentor engineers and help evolve Klaviyo’s ML engineering best practices, tooling, and architecture.
- Foster a culture of ownership, experimentation, and data-driven innovation across teams.
Who You Are
- 7–10 years of experience in machine learning engineering or applied software engineering, with a focus on classical ML and predictive modeling.
- Deep understanding of supervised learning methods such as random forests, XGBoost, logistic regression, and ensemble techniques.
- Proven track record of building and scaling production-grade ML systems end to end (data pipelines, training, deployment, and monitoring).
- Experienced backend engineer comfortable with distributed systems and high-scale architectures.
- Proficient in Python and modern frameworks (FastAPI, Django preferred).
- Skilled with big data and streaming technologies (Apache Spark, Kafka, Hadoop).
- Hands-on with asynchronous processing and distributed task queues (Celery, SQS, RabbitMQ, Redis).
- Strong understanding of databases and ORMs (SQLAlchemy, Alembic) and cloud-native architectures (AWS, Kubernetes, CI/CD).
- Excellent communicator who can set technical direction, influence product strategy, and mentor junior engineers.
- Curious, pragmatic, and passionate about building AI systems that drive measurable business impact.
Nice to Have
- Experience training and deploying ML models in production environments that generated measurable impact.
- Familiarity with reinforcement learning or agentic AI systems that adapt based on outcome or reward signals.