Lead Machine Learning Engineer

$172,300 - $231,100/Yr

Unclassified - Dallas, TX

Full-time

Dallas, TX

About the position

At Disney Entertainment & ESPN Technology, we are dedicated to reimagining the way audiences experience the world's most beloved stories. Our mission is to transform Disney's media business for the future by evolving our streaming and digital products in innovative and immersive ways. This includes powering worldwide advertising and distribution to maximize flexibility and efficiency, as well as delivering Disney's unmatched entertainment and sports content. Every day presents an opportunity to make a significant impact on our partners and the hundreds of millions of viewers around the globe. As part of the DE&E Technologists team, you will be involved in designing and building the infrastructure that will support Disney's media, advertising, and distribution businesses for years to come. The products and platforms developed by this group delight millions of consumers every minute, from Disney+ and Hulu to ABC News and ESPN. We pride ourselves on our commitment to innovation, developing groundbreaking products and techniques that shape industry standards and enhance audience experiences across sports, entertainment, and news. In this role, you will be a key contributor to the development, implementation, and maintenance of recommendation and personalization algorithms for Disney Streaming's suite of video apps, particularly Disney+ and Hulu. You will collaborate with Engineering, Product, and Data teams to apply machine learning methods that align with strategic product personalization goals. Your responsibilities will include leading research and development efforts for recommendation algorithms, coordinating requirements, managing stakeholder expectations, and ensuring that we meet key performance indicators (KPIs) for various product areas. You will also help set the roadmap for algorithmic work, addressing product requests for new recommendation features while driving larger company objectives in personalization and content recommendation.

Responsibilities

• Utilize cutting edge machine learning methods to develop and implement in production algorithms for personalization, recommendation, and other predictive 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.
• Gauge the complexity of machine learning problems and execute simple approaches for quick, effective solutions as appropriate.
• Communicate effectively with both technical and non-technical stakeholders.

Requirements

• Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience.
• 7+ years of experience developing machine learning models, performing large-scale data analysis, and/or data engineering experience.
• 5+ years writing production-level, scalable code (e.g., Python, Scala).
• 5+ 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 engineering big-data solutions using technologies like Databricks, S3, and Spark.

Nice-to-haves

• MS or PhD in statistics, math, computer science, or related quantitative field.
• Production experience with developing content recommendation algorithms at scale and familiarity with metadata management, data lineage, and principles of data governance.
• Experience loading and querying cloud-hosted databases.
• Building streaming data pipelines using Kafka, Spark, or Flink.

Benefits

• Medical, financial, and other benefits depending on the level and position offered.
• Bonus and/or long-term incentive units may be provided as part of the compensation package.