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Overview:

On any given day at Disney Entertainment & ESPN Technology, we’re reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming Disney’s media business for the future. Whether that’s evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize flexibility and efficiency, or delivering Disney’s unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world.

A few reasons why we think you’d love working for Disney Entertainment & ESPN Technology

• Building the future of Disney’s media business: DE&E Technologists are designing and building the infrastructure that will power Disney’s media, advertising, and distribution businesses for years to come.

• Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.

• Innovation: We develop and execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.

The vision of the Machine Learning (ML) Engineering team at Disney is to drive and enable ML usage across several domains in heterogeneous language environments and at all stages of a project’s life cycle, including ad-hoc exploration, preparing training data, model development, and robust production deployment. The team is invested in continual innovation on the ML infrastructure itself to carefully orchestrate a continuous cycle of learning, inference, and observation while also maintaining systems with availability and reliability. We seek to find new ways to scale with our guest and partner base as well as the ever-growing need for ML and experiments.

Job Summary:

We’re looking for a principal ML engineer interested in building optimized, efficient and reliable products, services and interfaces to federate access to data stores, simplify, automate and manage the ML development and deployment ecosystem. In addition, the ideal candidate would be excited about partnering with various data science, analytic and experimentation teams to gather requirements, seek feedback, and focus on continuous development and improvement of the ML infrastructure.

Responsibilities and Duties of the Role:

• Drive and architect ML-focused solutions for diverse domains including personalization, search, capacity planning and management, or fraud and abuse prevention.

• Design and develop tools, services, SDKs to simplify development, deployment and productionization of ML models at scale

• Experience deploying, maintaining and evaluating production ML models at scale.

• Collaborate with ML and data practitioners to automate their pipelines.

• Build tooling and low-latency services to enable and support event-driven pipelines.

• Defines best practices and standards for writing, building, testing, and deploying ML applications.

• Work on complex, cross-functional projects that have a broad impact on the business.

• Maintain a culture of quality, innovation and experimentation

• Work in an Agile environment that focuses on collaboration and teamwork.

Basic Qualifications:

• 10+ years of software experience working in large scale, real-time distributed systems.

• Experienced with cloud technologies in AWS or GCP as well as container systems such as Docker or Kubernetes.

• Passion for turning ideas into products and improving the member experience.

• Excellent communication and people engagement skills.

Preferred Qualifications

• Familiarity with ML pipelines and AWS technologies.

• Experience with graph-based data workflows such as Apache Airflow, Meson.

• Mentor colleagues on best practices and technical concepts of building large scale solutions.

Required Education

• 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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