Senior Machine Learning Engineer

Iterable is hiring a Senior Machine Learning Engineer in San Francisco, CA (onsite) to design and deliver production ML systems across the product.

Responsibilities

• Develop and ship core ML capabilities for product intelligence, including send-time and frequency optimization, recommendation engines, and campaign personalization
• Own the full model lifecycle end to end: EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation (multi-armed bandits, ensemble models)
• Build and scale data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes
• Partner with Backend and Platform teams to strengthen feature serving, improve data architecture, and establish engineering standards across the ML team
• Mentor engineers and help set the technical direction for ML at Iterable

Requirements

• 5+ years of hands-on machine learning engineering experience deploying complex models in production at scale
• Databricks & Spark proficiency from real production pipeline work using Databricks
• Production-level model engineering background: demonstrated ability to take deep learning and ensemble architectures from training through optimization to production deployment
• Strong Python and/or Scala skills, with an emphasis on readable, modular, maintainable production code
• Systems and infrastructure experience: hands-on work with microservice backends, distributed compute (Ray/Spark), and containers (Kubernetes)
• Pragmatism focused on serving latency, reliability, and measurable customer impact over novel theory

Technologies

• Databricks
• Spark
• AWS
• Ray
• Kubernetes
• Python
• Scala

Bonus Points

• Experience working on large-scale recommendation systems
• Background setting up feature stores or model observability tools
• Exposure to Infrastructure-as-Code (Terraform, Pulumi)

What You Might Work On

• Building, evaluating, and integrating new models that add intelligence to Iterable’s core product
• Enhancing models that optimize send time and frequency for outgoing message campaigns
• Designing data pipelines that make it easier to implement and test new models
• Improving feature- and model-serving architecture in collaboration with platform engineers
• Benchmarking and improving multi-armed bandit implementations
• Building tools to visualize and debug features and models

Location

• US

Compensation Range

• $150K - $250K per year