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# Senior Machine Learning Engineer - Disney Streaming in Glendale

- **Company:** Energy Jobline
- **Location:** Glendale, CA, United States
- **Experience:** Expert
- **Salary:** $148,700.0 - $199,400.0
- **Contract:** Permanent contract
- **Skills:** Amazon S3, Big Data, Information Engineering, Python (Programming Language), Machine Learning, SQL Databases, Data Streaming, Apache Spark, Deep Learning, Databricks
- **Published:** August 5, 2026
- **Apply:** https://www.energyjobline.com/job/senior-machine-learning-engineer-disney-streaming-glendale-31333690

## About the Role

5+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience
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5+ years writing production-level, scalable code (Python, SQL)
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3+ years of experience developing algorithms for deployment to production systems
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In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings
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Experience deploying and maintaining pipelines and in engineering big-data solutions using technologies like Databricks, S3, and Spark, Ability to gauge the complexity of machine learning problems and a willingness to execute simple approaches for quick, effective solutions as appropriate

## Description

As a member of this team you will collaborate across Engineering, Product, and Data teams to apply machine learning methods to meet strategic product personalization goals, explore innovative, cutting edge techniques that can be applied to recommendations, and constantly seek ways to optimize operational processes., This is an Individual Contributor role. You will be expected to lead recommendation and personalization algorithm research, development, and productionization for product areas, and to coordinate requirements and manage stakeholder expectations with Product, Engineering, and Editorial teams. As an IC, you will also be responsible for helping to set the roadmap for algorithmic work - not only for how to approach product requests for new recommendation features, but for helping to drive larger company objectives in the areas of personalization and recommendations.


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