Lead Machine Learning Engineer

Job  |
Location Glendale, California, United States / Santa Monica, California, United States / Seattle, Washington, United States |
Business Disney Entertainment and ESPN Product & Technology |
Date posted Jun. 01, 2026

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, innovators, creators, 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.

The Ad Platform Engineering organization builds, enhances, and operates a high‑performance, distributed, microservice‑based digital advertising platform. This platform powers billions of real‑time ad decisions daily across Disney’s video‑on‑demand and live TV properties, including Hulu, Disney+, ESPN, and more. Our programmatic teams maintain Disney’s programmatic advertising suite, the Real‑time Ad Exchange (DRAX), an award‑winning proprietary supply‑side platform that brings demand from multiple third‑party sources into Disney’s ad server in real time.

As a Lead Machine Learning Engineer, you will serve as a hands‑on technical leader responsible for delivering high‑impact machine learning systems while guiding technical direction within your domain. You will design, build, and operate production ML systems at scale, mentor engineers, and partner closely with product and engineering leaders to ensure machine learning solutions are reliable, performant, and aligned with business goals.

• Lead the design and delivery of machine learning solutions across advertising use cases such as inventory forecasting, pricing, targeting, and efficient ad delivery.
• Apply modern machine learning techniques to solve complex, real‑time advertising problems.
• Provide technical leadership for ML system architecture, modeling approaches, and production readiness within your domain.
• Design, build, and scale ML architectures that balance model quality, latency, throughput, reliability, and cost.
• Oversee the full ML lifecycle for owned systems, from experimentation through production deployment and iteration.
• Design and maintain feature pipelines and feature stores supporting both real‑time inference and offline training.
• Partner with product and engineering stakeholders to translate requirements into clear technical plans and measurable outcomes.
• Interpret experimental results and guide data‑informed decision‑making.
• Ensure ML systems are observable, debuggable, and explainable in production.
• Establish and maintain monitoring for model performance, drift, bias, and system health.
• Champion engineering excellence through best practices in code quality, system design, testing, and operational reliability.
• Mentor and support engineers through code reviews, design discussions, and ongoing technical guidance.

• Bachelor’s in Computer Science or equivalent practical experience.
• 7+ years of software engineering experience.
• 5+ years of hands‑on experience developing and deploying machine learning systems in production.
• Strong knowledge of machine learning fundamentals, mathematics, and statistics.
• Experience operating ML systems in low‑latency, high‑throughput environments.
• Strong communication and collaboration skills with both technical and non‑technical partners.
• Solid foundations in algorithms, data structures, and numerical optimization.
• Proficiency in Python (primary), with experience in Java and SQL.
• Experience with ML frameworks and tooling such as Tensor Flow, PyTorch, and Hugging Face.
• Experience with one or more of the following:
  • Deep learning methodologies (e.g., sequence‑based or representation learning models)
  • Transformer…