Senior ML Engineer — Production-Scale Intelligence

Iterable; San Francisco (CA); Presencial; USD 150.000 - 250.000; Jornada completa

Descripción de la vacante

Iterable in San Francisco, CA is seeking an experienced Machine Learning Engineer to design, build, and operate the full ML lifecycle, from data exploration and feature engineering to distributed training, deployment, and online experimentation. You will build and ship ML capabilities like send-time optimization, recommendations, and campaign personalization.

The role involves owning the end-to-end lifecycle, building scalable data pipelines, collaborating with Backend and Platform teams, and

Formación

• 5+ years hands-on ML engineering in production.
• Strong Databricks and Spark experience.
• Proven ability to move deep learning and ensemble architectures from training through production deployment.
• Strong Python and/or Scala skills with focus on readable, modular code.
• Experience with systems and infrastructure, including microservice backends and containerized deployments.

Responsabilidades

• Build and ship key ML capabilities used in the core product, including send-time and frequency optimization, recommendation engines, and campaign personalization.
• Own the end-to-end model lifecycle: EDA, feature engineering, distributed training, production deployment, latency monitoring, and online experimentation.
• Build and scale data pipelines and serving architectures for production workloads using Databricks, Spark, AWS, Ray, and Kubernetes.
• Partner with Backend and Platform teams to improve feature serving and data architecture, and help establish engineering standards.
• Mentor engineers and contribute to the technical direction for machine learning at Iterable.

Conocimientos

MLE Production

Python/Scala

Databricks

Spark

Distributed Compute

Latency & Reliability

Herramientas

Databricks

Spark

AWS

Ray

Kubernetes

Python

Scala

Descripción del empleo

Iterable in San Francisco, CA is seeking an experienced Machine Learning Engineer to design, build, and operate the full ML lifecycle, from data exploration and feature engineering to distributed training, deployment, and online experimentation. You will build and ship ML capabilities like send-time optimization, recommendations, and campaign personalization.

The role involves owning the end-to-end lifecycle, building scalable data pipelines, collaborating with Backend and Platform teams, and