Lead Machine Learning Engineer

Disney Entertainment & Sports LLC is building personalization experiences powered by modern machine learning and AI. In this role, you will help ideate, develop, iterate on, and productionize personalization algorithms across the recommendation stack, taking ownership of how recommendation systems are modeled, evaluated, and shipped to production.

Based in San Francisco, CA (onsite), you will drive innovation across Disney’s personalization workflow, including ranking, modeling, candidate retrieval, post-ranking, experimentation, and production-ready ML engineering.

What you will do

• Ideate, develop, iterate on, and productionize personalization algorithms spanning core ranking, content and user understanding models and graphs, candidate retrieval, and post-ranking systems.
• Apply modern AI and LLM techniques to recommendation systems, including generating and improving recommendations, strengthening system evaluation, and accelerating how models are built and improved.
• Contribute ideas on recommendation approaches, evaluation methodology, and how models define data, features, and objectives.
• Support other scientists in shaping and productionizing their ideas for personalization.
• Help define the technical vision and innovation agenda for personalization by identifying high-impact opportunities and guiding the team’s approach.
• Design and run rigorous offline and online experiments, and improve evaluation systems and methodology.
• Partner across Engineering, Product, and Data, clearly communicating methodologies to technical and non-technical audiences and managing stakeholder expectations.
• Build production-worthy, maintainable systems with strong standards for development, testing, and deployment, and step in to support production issues when needed.

Required qualifications

• 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 understanding of the modern AI landscape.
• Proficiency with PyTorch, TensorFlow, Databricks, Spark, and SQL.
• In-depth understanding of modern machine learning methods, models, and their mathematical underpinnings.
• Strong written and verbal communication skills.
• Collaborative, personable working style, with the ability to work effectively within and across teams rather than in isolation.

Technologies

• PyTorch
• TensorFlow
• Databricks
• Spark
• SQL
• AI
• LLM

Preferred qualifications

• PhD in computer science, statistics, math, or a related quantitative field.
• Publications or papers in machine learning or AI, especially in recommender systems.
• Production 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.

Education

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

Compensation: USD 187,900 - 252,000 per year.