Lead Machine Learning Engineer

Overview

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.

• Building the future of Disney’s media: designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come.
• Reach, Scale & Impact: our technology and products serve as a signature doorway for fans’ connections with Disney’s brands and stories (Disney+, Hulu, ESPN, ABC, ABC News, and more).
• Innovation: developing and implementing groundbreaking products and techniques that shape industry norms and solve complex technical problems.

We are looking for a Lead Machine Learning Engineer to ideate, develop, iterate on, and product ionize personalization algorithms across the recommendation stack—core ranking, content and user understanding models and graphs, candidate retrieval, and post‑ranking systems. The role blends applied science and end‑to‑end ML engineering, requiring the ability to prototype, validate, ship, and maintain scalable systems while collaborating with product, engineering, and data partners.

• Ideate, develop, iterate on, and product ionize personalization algorithms, including core ranking, content and user understanding models and graphs, candidate retrieval, and post‑ranking systems.
• Apply modern AI and LLM techniques to recommendation systems, generating and improving recommendations, strengthening evaluation, and accelerating model development.
• Contribute ideas and insights on recommendation approaches, evaluation methodology, and data/feature/goal definition, helping other scientists bring their ideas to life.
• Drive the technical vision and innovation agenda for personalization, identifying high‑impact opportunities and shaping team approach.
• Design and run rigorous offline and online experiments, improving evaluation systems and methodology.
• Build production‑worthy, maintainable systems that are easy to iterate on, uphold strong standards for development, testing, and deployment, and support production issues as needed.

• 7+ years of experience developing machine learning models and deploying them to production systems.
• Strong background in applied ML science, end‑to‑end ML engineering, or a blend of both, with experience in recommendation systems modeling.
• Hands‑on experience with AI and LLM techniques and a solid understanding of the modern AI landscape.
• Proficiency with tools and frameworks such as PyTorch, Tensor Flow, Databricks, Spark, and SQL.
• In‑depth understanding of modern machine learning methods, models, and their mathematical underpinnings.
• Strong written and verbal communication skills.
• A collaborative, personable working style; works well within the team and across teams rather than operating in isolation.

• PhD in computer science, statistics, math, or a related quantitative field.
• Publications or papers in machine learning or AI, especially in recommender systems.
• Experience developing content recommendation algorithms at scale.
• Experience with reinforcement learning or related sequential decision‑making approaches.
• Experience with evaluation methodology for recommendation systems, including offline evaluation and A/B experimentation.

• BS or MS in Computer Science, Engineering, or a related field.

The hiring range for this position in San Francisco, CA is $ - $ per year. The base pay actually offered will take into account internal equity and may vary depending on the candidate’s geographic region, job‑related knowledge, skills, and experience among other factors. A bonus and/or long‑term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.

San Francisco,CA,USA (Alternate locations: WA – 9254thAve, USA).