Lead Machine Learning Engineer

Amperity Amperity · Seattle · Seattle, WA · Engineering

Lead Machine Learning Engineer at Amperity, an AI-first company focused on customer data and personalized experiences. The role involves leading ML projects, guiding technical direction, and developing platform capabilities for AI-driven products. Responsibilities include architecting ML platform components, building automated training and deployment pipelines, designing feature engineering systems, improving inference latency, and establishing MLOps best practices. Requires 8+ years of experience in building production ML systems, technical leadership, expertise in ML deployment, serving, feature engineering, monitoring, and cloud-native infrastructure.

What you'd actually do

  1. Architect ML platform components—feature stores, model registries, and serving infrastructure—that help teams across the organization to deploy models reliably and at scale.
  2. Build automated training and deployment pipelines that support model improvement for data drift and model degradation.
  3. Design real-time and batch feature engineering systems that power identity resolution, customer segmentation, and predictive models at enterprise scale.
  4. Improve model inference latency to deliver ML predictions that meet strict Service level agreements while managing infrastructure costs.
  5. Establish MLOps best practices, SLOs, and operational standards that ensure production ML systems are reliable, observable, and maintainable.

Skills

Required

  • Python
  • ML frameworks (e.g. XGBoost, PyTorch, PySpark)
  • Cloud-native ML infrastructure
  • Containerization
  • Orchestration (Kubernetes, Docker)
  • ML deployment patterns
  • Model serving
  • Feature engineering
  • Monitoring/observability for ML systems
  • Technical leadership
  • ML platform development
  • MLOps

Nice to have

  • Apache Spark
  • Presto
  • Kafka
  • MLflow
  • Feature stores
  • Model serving frameworks
  • Terraform
  • Clojure
  • Entity resolution
  • Classification
  • Customer analytics
  • AI coding assistants

What the JD emphasized

  • 8+ years of experience building production ML systems
  • Technical leadership experience driving ML platform evolution or major ML projects across multiple teams
  • Expertise in ML deployment patterns, model serving, feature engineering, and monitoring/observability for ML systems
  • Software engineering skills with experience in Python and familiarity with ML frameworks (e.g. XGBoost, PyTorch, PySpark)
  • Experience with cloud-native ML infrastructure, containerization, and orchestration (Kubernetes, Docker)

Other signals

  • Architect ML platform components
  • Build automated training and deployment pipelines
  • Design real-time and batch feature engineering systems
  • Improve model inference latency
  • Establish MLOps best practices