Lead Machine Learning Engineer
About The Position
Disney Entertainment and ESPN Product & Technology is seeking a Lead Machine Learning Engineer to join the Core ML team. This team is responsible for the core personalization algorithms that power Disney+ and Hulu, working at the intersection of research and production. The role involves ideating, prototyping, validating, and shipping solutions that enhance the personalization experience for millions of users globally. The team focuses on areas such as real-time ranking, content and user understanding, candidate retrieval, and post-ranking. This is an opportunity to work on cutting-edge AI and LLM techniques to improve recommendation systems and the processes used to build and evaluate them. The Lead Machine Learning Engineer will help drive the vision and innovation behind Disney's personalization systems, aiming to delight users with great content recommendations, improve customer satisfaction, and deepen the understanding of both content and users.
Requirements
- 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 ideally 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, TensorFlow, 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
- BS or MS in Computer Science, Engineering, or a related field
Nice To Haves
- 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
Responsibilities
- Ideate, develop, iterate on, and productionize 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, including using them to generate and improve recommendations, strengthen system evaluation, and accelerate how we build and improve our models.
- Contribute ideas and insight on recommendation approaches, evaluation methodology, and how we define data, features, and objectives for our models, and help other scientists on the team shape and productionize their ideas.
- Help drive the technical vision and innovation agenda for personalization, identifying high-impact opportunities and shaping how the team approaches them.
- Design and run rigorous offline and online experiments, and contribute to improving our evaluation systems and methodology.
- Work closely within the team and across Engineering, Product, and Data partners, communicating methodologies clearly to technical and non-technical audiences and managing stakeholder expectations.
- Build production-worthy, maintainable systems that are easy to iterate on, uphold strong standards for development, testing, and deployment, and jump in to support when production issues arise.
Benefits
- A bonus and/or long-term incentive units may be provided as part of the compensation package
- the full range of medical, financial, and/or other benefits, dependent on the level and position offered
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Associate degree
