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 high system availability and reliability. We seek to maximize the positive business impact of all ML at Disney streaming by supporting key product functions like personalization and recommendation, fraud and abuse prevention, capacity planning, subscriber growth and lifecycle intelligence, and so on.

We’re looking for an engineering leader interested in leading the Model Engineering team (part of ML Platform) that builds interfaces, tooling and services to develop and deploy ML models, host and manage them in a high-availability and low-latency production ecosystem. The leader will drive the technical strategy, partner with platform partners and stakeholders, seek feedback, and focus on continuous development and improvement of the ML runtime, and simplified user onboarding.

Responsibilities:

• Lead and grow a team to build state of the art ML runtime environment
• Provide technical direction for the Model Engineering team
• Partner with Product and stakeholders to deliver innovative platform solutions for scalable ML development and deployment
• Mentor and facilitate individual careers by optimizing for their success and growth
• Maintain a culture of innovation, quality, transparency, inclusion, and empathy
• 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
• 2+ years of leadership experience
• Experience building and deploying ML models in production
• Experience with cloud technologies in AWS or GCP as well as container systems such as Docker or Kubernetes
• Passion for building platforms and infrastructure excellence
• Excellent communication and people engagement skills

Nice to have:

• Familiarity with ML pipelines, data ecosystem and AWS technologies
• Building ML infrastructure, streaming ML applications
• Experience shipping entertainment and media applications for streaming purposes

Education:

Bachelor’s degree in Computer Science

Job Posting Statement The hiring range for this position in Seattle, Washington or New York is $212,600 - $285,100 per year, in Los Angeles, California is $202,900 - $272,100 per year and in San Francisco, California is $222,200 - $297,900 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.