Disney Entertainment & ESPN Technology
On any given day at Disney Entertainment & ESPN Technology, we are 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 is 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 would love working for Disney Entertainment & ESPN Technology
- Building the future of Disney's media business: DE&E (Disney Entertainment & ESPN) 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. In this role you will work on event and context processors to federate data, infrastructure and tooling to enable event-driven ML pipelines. You will own and expand part of our central feature store that powers ML use cases in domains like recommendations, search and fraud. You will work on cross-functional projects and push the envelope on data and ML infrastructure.
Responsibilities:
- Develop and improve feature store tooling and services, and contribute to the ML infrastructure development
- Collaborate with ML practitioners to build streaming data ecosystem
- Develop low-latency services to enable and support event-driven pipelines
- Ability to work on multi-faceted projects with engineers from diverse backgrounds, heterogenous skills and across teams
- Drive and maintain a culture of quality, innovation and experimentation
- Work in an Agile environment that focuses on collaboration and teamwork
Basic Qualifications:
- Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience
- 7+ years of software experience working in large scale, real-time distributed systems
- Experience building and deploying big data pipelines in production
- Experience with cloud technologies in AWS (Amazon Web Services) 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
Preferred Qualifications:
- Familiarity with ML pipelines, data ecosystem and AWS technologies
- Building ML infrastructure, streaming ML applications
- Experience shipping entertainment and media applications for streaming purposes
The hiring range for this position in New York is $156,300-209,600$ per year and in San Francisco is $163,500-$219,200 per year and Los Angeles is $149,300-$200,200per 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.
