Lead Machine Learning Engineer

The Walt Disney Company•Glendale, CA

About The Position

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. The Ad Platform Engineering organization within Disney Entertainment and ESPN Product & Technology is responsible for building, enhancing, and operating 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. Within Ad Platform Engineering, the Programmatic teams build and maintain Disney’s programmatic advertising suite of products and services that comprise Disney's Real-time Ad Exchange (DRAX). DRAX is an award-winning, proprietary supply-side platform (SSP) that enables programmatic deal configuration and integrates 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. This is a production-focused leadership role, blending deep technical execution with domain-level technical ownership and mentorship. Daily, you should bring: Strong technical ownership of ML systems and accountability for outcomes The ability to lead by example through hands-on design, implementation, and operational excellence Clear and effective communication across engineering, product, and data partners Comfort translating ambiguous business problems into well-scoped technical solutions A focus on system performance, reliability, scalability, and cost efficiency A collaborative, pragmatic, and optimistic approach to leading complex initiatives A passion for mentoring, learning, and adapting to a very dynamic and fast-paced environment

Requirements

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 TensorFlow, PyTorch, and Hugging Face
Experience with one or more of the following: Deep learning methodologies (e.g., sequence-based or representation learning models) Transformer architectures (e.g., BERT, GPT, ViT) for NLP and/or vision Multimodal embedding techniques across text, image, audio, or structured data Large language models and related evaluation methodologies Retrieval-augmented generation (RAG) architectures
Experience building systems on cloud-native infrastructure and distributed platforms
Proven ability to thrive in a fast-paced, data-driven, and collaborative environment

Nice To Haves

MS or PhD (preferred) in Computer Science or equivalent practical experience
Experience in digital video advertising or the digital marketing domain
Experience with programmatic advertising or real-time bidding platforms

Responsibilities

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