Director, Machine Learning Engineering

Job Posting Title:

Director, Machine Learning Engineering

Req ID:

10121877

Job Description:

About Direct To Consumer

Disney Direct to Consumer (DTC) includes premium streaming services with Hulu, a premium streaming service that offers premium originals, current season TV, a massive library of hit series and movies, and live television and Disney+ is the Disney-branded streaming service featuring an incomparable collection of content from its brands and franchises recognized and respected all over the world including Star Wars, Marvel, Pixar, Disney, and NatGeo.

About The Role

The Machine Learning Engineering (MLE) team enables Data Scientists to build, deploy, and scale machine learning solutions across a broad range of use cases—Personalization, Forecasting, Marketing, Content and Audience Segmentation, Payments Optimization, Fraud Prevention, Live Time Value prediction, and more. MLE partner closely with Data Scientists to design scalable data pipelines, launch performant models, and maintain critical ML systems in production. These efforts drive decisions that impact millions of consumers and generate significant revenue for The Walt Disney Company.

The Director of MLE leads a high-performing team of Data Science and ML engineers and managers who design, build, and support our unified ML platform. This includes developing AutoML capabilities, development and maintenance of Feature Mart, enabling end-to-end ML Ops workflows, and implementing robust model monitoring and drift detection. The Director also collaborates across departments to ensure infrastructure aligns with business needs and explores emerging technologies, including Generative AI, to keep Disney at the forefront of applied machine learning.

Responsibilities

• Unified ML Platform Development
  Lead the development of a standardized, reusable ML platform (AutoML) to support rapid model development for Data Scientists. Ensure scalability, flexibility, and integration with enterprise systems.

• ML Ops & Deployment Infrastructure
  Drive the implementation of robust deployment pipelines, model versioning, testing frameworks, and CI/CD systems to support production-grade ML services.

• Production Monitoring & Model Integrity
  Establish systems for automated monitoring, drift detection, performance evaluation, and alerting. Act as the first line of defense for live model health and data quality assurance.

• Technology Innovation & Integration
  Stay on the cutting edge of ML technologies—including Generative AI, foundation models, vector databases, and LLMOps. Assess, pilot, and integrate relevant innovations that enhance modeling workflows or business capabilities.

• Data Science & Business Impact Alignment
  Bring a strong foundation in applied data science to collaborate effectively with business stakeholders and DS teams. Translate platform capabilities into business impact through hands-on understanding of model development, performance metrics, and experimentation.

• Leadership & Talent Development
  Hire, mentor, and manage a diverse team of ML engineers and managers. Foster a high-performing, inclusive culture focused on technical excellence, agility, and innovation. Lead others through example and inspiration, bring a can-do attitude, and deliver magic to your team and business partners.

• Cross-Functional Collaboration
  Partner with senior leaders across Data Science, Engineering, Product, Legal, and Governance to align on objectives, ensure compliance, and scale impact across the organization.

Required Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field

• 12+ years of relevant experience

• Strong foundation in applied data science, with experience building or supporting ML models in a business context

• Experience with Generative AI or interest in assessing and integrating emerging AI technologies

• Prior experience (5+ years) leading DS/ML engineering or ML platform teams;

• Demonstrated expertise in ML Ops, AutoML, and productionizing machine learning at scale

• Familiarity with data ecosystems (e.g., SnowFlake, DataBricks), and modern ML tooling

• Strong communication, stakeholder management, and team-building skills

• Experience navigating privacy, compliance, and governance in machine learning workflows

Preferred Qualifications

• Master's degree or PhD in Computer Science, Engineering, Mathematics, or a related field

Additional Information

#DISNEYTECH

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