Software Engineer II

Technology is at the heart of Disney’s past, present, and future. Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for Disney’s media business globally. The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses. We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company’s media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world. The Observability & Insights group ensures that Disney Streaming’s distributed systems are reliable, performant, and transparent. We build ML-powered detection systems, telemetry pipelines, intelligent alerting, and developer experience tooling that enable engineers across the organization to understand system health and take action quickly. As a Software Engineer II, you will contribute to building and operating machine learning models and AI-driven systems that enhance the reliability of Disney’s streaming ecosystem. You will work on production ML models — including autoencoders for anomaly detection, statistical threshold systems, and LLM-powered investigation gates — that transform telemetry and signals into automated detection and proactive insights across Disney+, Hulu, and ESPN. You will participate in the ML lifecycle: feature engineering on time-series data, model training on GPU clusters, real-time inference pipelines, and model improvement. You will partner with engineering and platform teams to embed intelligence into operational workflows, improving system resilience and customer experience at scale. As a Software Engineer II, you will deliver features end-to-end, participate in model design and code reviews, and grow into owning components of production ML systems within a fast-paced, AI-native engineering environment.