Job Summary:
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.
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 be expected to work in the ML Platform team that builds interfaces, tooling, and services to develop and deploy ML models. You will host and manage these models in a high-availability and low-latency production ecosystem, providing 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. Additionally, you will work on cross-functional projects and push the envelope on data and ML infrastructure.
What You Will Do
ML Platform Development and Maintenance: Lead the enhancement or development of a machine learning platform to apply state-of-the-art AI-based technologies. This platform will support training, inference, and all aspects of machine learning through a self-service interface.
Feature Engineering and Optimization: Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins; deploy scalable streaming and batch data pipelines to support petabyte scale datasets
Development Best Practices: Maintain existing standards and drive the development, testing, and deployment of new platform features
Collaborate with product and business stakeholders: Collaborate with researchers, program managers, product managers, and SDETs in an open and innovative environment. Mentor and guide the professional and technical development of your team members.
What You Will Bring
7+ years of professional programming and design experience in Java, Python, Scala, etc.
Bachelor’s Degree in Computer Science, or related quantitative field or comparable field of study, and/or equivalent work experience.
Strong experience for ML technologies with technical stacks like TensorFlow, Kubeflow, PyTorch, Databricks, AWS SageMaker, etc.
Experience with big data processing and bigdata technologies
Strong knowledge of system / application design and architecture
Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and in engineering big-data solutions using technologies like Databricks, S3, and Spark
Strong written and verbal communication skills
MS or PhD in computer science, or comparable field of study, and/or equivalent work experience
Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment
Familiar with metadata management, data lineage, and principles of data governance
Experience loading and querying cloud-hosted databases
Building streaming data pipelines using Kafka, Spark, or Flink
Experience with: AWS, Docker, Airflow, Databricks
#DISNEYTECH
The hiring range for this position in Santa Monica, California is $167,700- $224,900 per year and in San Francisco, California is $183,700- $246,400 per year. The hiring range for this position in New York and Seattle, Washington is $175,800 - $235,700 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.