Lead Machine Learning Engineer - Infrastructure

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.

Job Summary:

As a core contributor within our Machine Learning (ML) organization, you will lead the research, development, deployment, and optimization of ML applications, collaborating closely with cross-functional teams including Engineering, Product, Data, and Editorial. Your work will directly support strategic initiatives to help shape the roadmap for algorithmic innovation while ensuring that solutions are scalable, impactful, and aligned with stakeholder needs. In addition to execution, you will contribute to the ML Lab team’s broader mission: enabling the use of machine learning across heterogeneous environments and at every stage of a project’s lifecycle—from ad-hoc exploration to robust production deployment. This includes partnering with engineering and service teams to:

• Drive infrastructure innovation for scalable learning, inference, and monitoring
• Provide ML consultancy and mentorship
• Conduct in-depth data exploration and analysis

Responsibilities:

• Design and develop infrastructure supporting the full cycle of machine learning, including workflow orchestration and management interfaces, data discovery tools, data quality and feature libraries.
• Drive data and ML-driven solutions for diverse engineering use cases such as recommendation systems, object detection, anomaly detection, RAGs and translations
• Identify impactful opportunities to improve our business operations and develop practical solutions and plans to lift our business KPIs
• Provide technical leadership to a team of engineers and work collaboratively with peers to achieve goals within deadlines.

Basic Qualifications:

• BS in computer science, statistics, math or a related quantitative field + 7 years of relevant SWE and MLEng experience
• Expertise in data science, (deep) learning algorithms, or statistical methods to solve real-world engineering problems
• Comfortable operating at all levels of the predictive stack, including data collection, feature engineering, batch training and low-latency online serving
• Experience designing and developing backend microservices for large-scale distributed systems using gRPC or REST
• Experience with large-scale distributed data processing systems, cloud infrastructure such as AWS or GCP, and container systems such as Docker or Kubernetes.
• Track record of building scalable systems, from design to full production
• Understanding of statistical concepts (e.g., hypothesis testing, regression analysis)
• Excellent written and oral communication skills

Preferred Qualifications:  

• Familiarity with developing and deploying Spark and ML pipelines
• Hands-on experience with big data technologies such as Hadoop, HDFS, Airflow, Databricks, Kinesis, Kafka
• Experience building backend microservices for large-scale distributed systems
• Drive and maintain a culture of quality, innovation and experimentation
• Mentor colleagues on best practices and technical concepts of building large scale solutions.

The hiring range for this position in New York or Seattle is $175,800 to $235,700 per year, in San Francisco is $183,700 to $246,400 per year, and in Los Angeles is $167,700 to $224,900 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.