Lead Machine Learning Engineer

Job Posting Title:

Lead Machine Learning Engineer

Req ID:

10154653

Job Description:

Disney Entertainment and ESPN Product & Technology

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. 

Here are a few reasons why we think you’d love working here:

• Building the future of Disney’s media: Our Technologists are designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come.
• Reach, Scale & Impact: More than ever, Disney’s technology and products serve as a signature doorway for fans' connections with the company’s brands and stories. Disney+. Hulu. ESPN. ABC. ABC News…and many more. These products and brands – and the unmatched stories, storytellers, and events they carry – matter to millions of people globally. 
• Innovation: We develop and implement groundbreaking products and techniques that shape industry norms, and solve complex and distinctive technical problems.

Product Engineering is a unified team responsible for the engineering of Disney Entertainment & ESPN digital and streaming products and platforms. This includes product engineering, media engineering, quality assurance, engineering behind personalization, commerce, lifecycle, and identity.

The Core ML team is an applied science and machine learning engineering team that owns the core personalization algorithms powering Disney+ and Hulu. Our work spans real-time ranking, content and user understanding, candidate retrieval, and post-ranking, serving recommendations to one of the largest streaming audiences in the world. We operate at the intersection of research and production: we ideate, prototype, validate, and ship, and we are responsible for driving the innovation that moves the personalization experience forward

Job Summary:

We are looking for a Lead Machine Learning Engineer to help us ideate, develop, iterate on, and productionize personalization algorithms across the recommendation stack. This includes our core ranking algorithms, content and user understanding models and graphs, as well as candidate retrieval and post-ranking systems.

There is more than one way to be a great fit for this role. You might be a strong applied scientist with sharp intuition for recommendation approaches, evaluation methodology, and how data, features, and objectives shape model behavior. You might be a strong end-to-end ML engineer who can take ideas to production at scale and keep systems healthy, maintainable, and easy to iterate on. Ideally, you bring a blend of both: someone who generates ideas of their own, helps other applied scientists bring theirs to life, and can jump in from either the science or the engineering side when something needs attention.

This is also an opportunity to work at the frontier. We are especially excited about candidates with strong relevant experience (RecSys, ML, AI/LLM) who can help bridge where recommendation systems are today and where the field is heading, applying modern AI techniques not only to improve recommendations themselves, but to improve how we build, evaluate, and iterate on our systems.

In this role, you will help drive the vision and innovation behind Disney's personalization systems, with the goal of delighting our users through great content recommendations, improving customer satisfaction, and deepening our understanding of both content and users.

Responsibilities:

• Algorithm development: Ideate, develop, iterate on, and productionize personalization algorithms, including core ranking, content and user understanding models and graphs, candidate retrieval, and post-ranking systems.
• AI and LLM innovation: Apply modern AI and LLM techniques to recommendation systems, including using them to generate and improve recommendations, strengthen system evaluation, and accelerate how we build and improve our models.
• Applied science: Contribute ideas and insight on recommendation approaches, evaluation methodology, and how we define data, features, and objectives for our