[Remote] Senior Machine Learning Engineer, Nova Intelligence

Note: The job is a remote job and is open to candidates in USA. Iterable is hiring a Senior Machine Learning Engineer to design and build end-to-end machine learning systems that power intelligence across its product. The role owns model development and production systems, including personalization, recommendation, data pipelines, model serving, and online experimentation.

Responsibilities

• Build and ship core product ML capabilities, including send-time/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 (e.g., multi-armed bandits, ensemble models)
• Build and scale our data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes
• Partner with Backend and Platform teams to elevate feature serving, improve overall data architecture, and establish engineering standards across the team
• Mentor engineers on the team and help set the technical direction for ML at Iterable
• Building, evaluating, and integrating new models that add intelligence into our core product
• Enhancing models that optimize send time and frequency of outgoing message campaigns
• Designing data pipelines that make it easy to implement and test new models
• Working with platform engineers to enhance our feature- and model-serving architecture
• Benchmarking and improving our multi-armed bandit implementation
• Building tools to visualize and debug features and models

Skills

• 5+ years of hands-on MLE experience putting complex models into production at scale
• Real experience building, optimizing, and scaling production data pipelines using Databricks
• Proven track record of taking deep learning and ensemble architectures from training through optimization to production deployment
• Strong Python and/or Scala skills with an emphasis on readable, modular, and maintainable systems code
• Hands-on work with microservice backends, distributed compute (Ray/Spark), and containers (Kubernetes)
• You care more about serving latency, reliability, and measurable customer impact than novel theoretical architectures
• Prior work on large-scale recommendation systems
• Experience setting up feature stores or model observability tools
• Exposure to Infrastructure-as-Code (Terraform, Pulumi)

Benefits

• Variable pay (where applicable)
• Equity
• Medical, dental, vision, and financial benefits
• Remote work in the US

Company Overview

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