Lead Machine Learning Engineer (Video)

Overview:

Technology is at the heart of Disney’s past, present, and future. Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for Disney’s media business globally.

The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses. We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company’s media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world.

Disney Entertainment & ESPN Product Technology:

On any given day at Disney Entertainment & ESPN Technology, we’re reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming Disney’s media business for the future. Whether that’s evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize flexibility and efficiency, or delivering Disney’s unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world.

A few reasons why we think you’d love working for Disney Entertainment & ESPN Technology:

• Building the future of Disney’s media business: DE&E Technologists are designing and building the infrastructure that will power Disney’s media, advertising, and distribution businesses for years to come.

• Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.

• Innovation: We develop and execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.

Job Summary:

As a core contributor within our Machine Learning (ML) organization, you will lead the research, development, deployment, and optimization of ML applications, collaborating closely with cross-functional teams including Engineering, Product, Data, and Editorial. Your work will:

directly support strategic initiatives to help shape the roadmap for algorithmic innovation while ensuring that solutions are scalable, impactful, and aligned with stakeholder needs.

In addition to execution, you will contribute to the ML-Lab team’s broader mission: enabling the use of machine learning across heterogeneous environments and at every stage of a project’s lifecycle—from ad-hoc exploration to robust production deployment. This includes partnering with engineering and service teams to:

• Drive infrastructure innovation for scalable learning, inference, and monitoring

• Provide ML consultancy and mentorship

• Conduct in-depth data exploration and analysis

Responsibilities:

• Drive data and ML driven solutions for diverse engineering use cases such as recommendation systems, object detection, anomaly detection, RAGs and translations

• Architect and build end to end ML models to drive data driven decisions

• Identify impactful opportunities to improve our business operations and develop practical solutions and plans to lift our business KPI’s

• Provide technical leadership to a team of engineers and work collaboratively with peers to achieve goals within deadlines

Basic Qualifications:

• BS in computer science, statistics, math or a related quantitative field + 7 years of relevant SWE and MLEng experience

• Expertise in data science, (deep) learning algorithms, or statistical methods to solve real-world engineering problems

• Comfortable operating at all levels of the predictive stack, including data collection, feature engineering, batch training and low-latency online serving

• Experience with large-scale distributed data processing systems, cloud infrastructure such as AWS or GCP, and container systems such as Docker or Kubernetes

• Track record of building scalable ML applications, from design to full production

• Understanding of statistical concepts (e.g., hypothesis testing, regression analysis)

• Excellent written and oral communication skills

Preferred Qualifications:

• Hands-on experience building models for video processing (such as clipping, cropping, understanding, search, …)

• Familiarity with developing and deploying Spark and ML pipelines