Senior ML Engineer — Production Personalization & Recommendations

Iterable, Inc.; United States; Remote; USD 180,000 - 240,000; Full time

Job summary

Iterable, Inc. is seeking a Senior Machine Learning Engineer to design and build end-to-end ML systems powering our AI-driven customer engagement platform. You will work with thousands of enterprise clients, enabling personalized experiences at scale.

You will own the full model lifecycle from experimentation to production, collaborating with Backend and Platform teams and mentoring engineers while advancing ML infrastructure in a fast-growing environment.

Qualifications

• 5+ years of hands-on MLE experience putting models into production at scale.
• Databricks & Spark mastery: real experience building, optimizing, and scaling production data pipelines using Databricks.
• Production-level Model Engineering: taking deep learning and ensemble architectures from training through production deployment.
• Production Code: strong Python and/or Scala skills with readable, modular code.
• Systems & Infra Experience: microservice backends, distributed compute (Ray/Spark), and containers (Kubernetes).

Responsibilities

• Build and ship core product ML capabilities (send-time/frequency optimization, recommendations, personalization).
• Own the full model lifecycle end-to-end: EDA, feature engineering, distributed training, production deployment, latency monitoring, online experimentation.
• Build and scale data pipelines and serving architectures using Databricks, Spark, AWS, Ray, and Kubernetes.
• Mentor engineers and help set the technical direction for ML at the company.

Skills

Databricks

Spark

Python

Scala

Production ML

Distributed compute

Kubernetes

Ray

Readable code

Tools

Terraform

Pulumi

Job description

Iterable, Inc. is seeking a Senior Machine Learning Engineer to design and build end-to-end ML systems powering our AI-driven customer engagement platform. You will work with thousands of enterprise clients, enabling personalized experiences at scale.

You will own the full model lifecycle from experimentation to production, collaborating with Backend and Platform teams and mentoring engineers while advancing ML infrastructure in a fast-growing environment.