Lead AI Software Engineer

Klaviyo Klaviyo · Enterprise · Boston, MA · Engineering

Lead AI Engineer responsible for designing, building, and scaling backend and ML systems for AI agents that optimize marketing and customer experience strategies. Focuses on predictive systems, autonomous learning agents, and production ML pipelines at a large scale.

What you'd actually do

  1. Lead the design and development of backend and ML systems that scale to serve 182K+ customers.
  2. Build robust, reliable, and efficient data processing and model-serving pipelines that power production ML models.
  3. Develop and optimize predictive systems (e.g., conversion likelihood, engagement ranking, personalization, anomaly detection).
  4. Architect systems and services that enable our AI agents to learn and improve autonomously from reward or outcome signals.
  5. Collaborate cross-functionally to translate business challenges into scalable, measurable ML solutions.

Skills

Required

  • machine learning engineering or applied software engineering
  • classical ML and predictive modeling
  • supervised learning methods such as random forests, XGBoost, logistic regression, and ensemble techniques
  • building and scaling production-grade ML systems end to end (data pipelines, training, deployment, and monitoring)
  • distributed systems and high-scale architectures
  • Python
  • big data and streaming technologies (Apache Spark, Kafka, Hadoop)
  • asynchronous processing and distributed task queues (Celery, SQS, RabbitMQ, Redis)
  • databases and ORMs (SQLAlchemy, Alembic)
  • cloud-native architectures (AWS, Kubernetes, CI/CD)
  • set technical direction
  • mentor junior engineers

Nice to have

  • FastAPI, Django
  • training and deploying ML models in production environments that generated measurable impact
  • reinforcement learning or agentic AI systems that adapt based on outcome or reward signals

What the JD emphasized

  • systems that can learn, run, and optimize themselves
  • AI agents that can automatically create, execute, and optimize marketing and customer experience strategies
  • systems and services that enable our AI agents to learn and improve autonomously from reward or outcome signals

Other signals

  • designing, building, and scaling backend and machine learning systems
  • AI agents that can automatically create, execute, and optimize marketing and customer experience strategies
  • predictive systems (e.g., conversion likelihood, engagement ranking, personalization, anomaly detection)
  • systems and services that enable our AI agents to learn and improve autonomously from reward or outcome signals
  • ML engineering best practices, tooling, and architecture