Machine Learning Engineer II

$118,000 - $158,200/Yr

Disney Entertainment & ESPN Technology - San Francisco, CA

Full-time - Mid Level

San Francisco, CA

About the position

The position at Disney Entertainment & ESPN Technology involves developing and optimizing recommendation and personalization algorithms for Disney's streaming services, including Disney+ and Hulu. As an Individual Contributor, you will lead research and development efforts, collaborate with various teams, and ensure that the algorithms meet strategic product goals while enhancing user experiences. This role is pivotal in shaping the future of Disney's media business through innovative technology and data-driven solutions.

Responsibilities

• Utilize cutting-edge machine learning methods to develop and implement algorithms for personalization and recommendation systems.
• Maintain algorithms deployed to production and explain methodologies to technical and non-technical teams.
• Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins.
• Deploy scalable streaming and batch data pipelines to support petabyte-scale datasets.
• Maintain existing and establish new algorithm development, testing, and deployment standards.
• Identify and define new personalization opportunities and collaborate with other data teams to improve data collection, experimentation, and analysis.

Requirements

• 3+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience.
• Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.
• 3+ years writing production-level, scalable code (e.g. Python, Scala).
• 3+ years of experience developing algorithms for deployment to production systems.
• In-depth understanding of modern machine learning (e.g. deep learning methods), models, and their mathematical underpinnings.
• Experience deploying and maintaining pipelines (AWS, Docker, Airflow) 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.
• Strong written and verbal communication skills.

Nice-to-haves

• MS or PhD in statistics, math, computer science, or related quantitative field.
• Production experience with developing content recommendation algorithms at scale.
• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
• Familiar with metadata management, data lineage, and principles of data governance.

Benefits

• Medical benefits
• Financial benefits
• Bonus and/or long-term incentive units may be provided as part of the compensation package.