JobsUSALead Machine Learning Engineer - ESPN+ Personalization
Company : The Walt Disney Company
Location : San Francisco, CA, 94199
Posted Date : 15 September 2025
Job Details
Lead Machine Learning Engineer - ESPN+ Personalization
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Lead Machine Learning Engineer - ESPN+ Personalization
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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.
ESPN is building a new real-time video recommendation system as a core capability of our next-generation streaming platform. We are building a foundational ML team that will power the personalization experience for millions of users.
We are seeking a Lead Machine Learning Engineer to take ownership of major components of the end-to-end personalization system. In this role, you will lead the technical design and delivery of key subsystems (modeling, data pipelines, real-time serving, or online experimentation), working closely with the Principal MLE, product, infrastructure, and cross-functional partners.
You will combine deep machine learning expertise with strong software engineering skills to drive production-grade ML solutions at scale. In addition, you will provide mentorship to more junior engineers and play a key role in establishing technical standards, development processes, and team culture as the team grows.
Responsibilities and Duties of the Role:
- Lead the design, development, and deployment of machine learning models for large-scale real-time short-form video recommendation.
- Architect and implement key subsystems of the end-to-end personalization pipeline, including model training, online inference, feature stores, streaming pipelines, and serving infrastructure.
- Build advanced recommendation models using deep learning, embeddings, sequence models, transformers, and multi-task learning frameworks.
- Partner with Principal ML Engineer and technical leadership to drive system architecture decisions balancing scalability, latency, accuracy, and maintainability.
- Conduct deep data analyses on user interactions to identify optimization opportunities and drive continuous model improvements.
- Drive ML experimentation processes, A/B testing, and evaluation frameworks to validate model performance.
- Establish and enforce ML engineering best practices across model development, deployment, monitoring, and governance.
- Mentor and provide technical guidance to other ML engineers, contributing to capability building within the team.
- Collaborate closely with product managers, data scientists, engineers, and infrastructure teams to align technical execution with business goals.
Basic Qualifications:
- Demonstrated ownership of end-to-end ML system components with successful production launches.
- Strong applied ML expertise with experience in personalization, recommendation systems, ranking models, and/or predictive modeling.
- Proficiency with modern ML frameworks such as TensorFlow, PyTorch, or similar.
- Experience with real-time feature stores, streaming data pipelines, and online inference architectures.
- Strong software engineering skills, with experience in distributed systems, data pipelines, and cloud platforms (AWS, GCP, Azure).
- Excellent communication, collaboration, and technical leadership skills, including mentorship experience.
- Experience partnering with cross-functional teams (product, infra, data science) to deliver ML-powered product features.
- Experience building real-time recommendation systems for content feeds, media platforms, or short-form video.
- Familiarity with ranking models, retrieval systems, approximate nearest neighbor search (ANN), and embedding management at scale.
- Knowledge of real-time personalization challenges including cold start, feedback loops, delayed labels, and exploration-exploitation tradeoffs.
- Experience with experimentation platforms (A/B tests, multi-armed bandits, reinforcement learning).
- Experience operating in startup-like or 0→1 product development environments.
- Ability to identify technical risks, balance tradeoffs, and drive pragmatic solutions under ambiguous product requirements.
- 7+ years of hands-on experience building and deploying machine learning models into production systems.
- Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience
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