Software Engineer

Lead Software EngineerDisney 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.As a Lead Software Engineer, you will design and build intelligent, AI driven systems that enhance the reliability and performance of Disney's large scale streaming ecosystem. You will develop agentic systems, machine learning models, and real-time pipelines that transform telemetry, logs, and user signals into automated detection, root cause analysis, and proactive insights.In this role, these autonomous agents will reason over complex system behavior, identifying issues in real time and driving faster resolution across Disney+, Hulu, and ESPN. You will partner closely with engineering, product, and platform teams to embed this intelligence directly into operational workflows, improving system resilience and subscriber experience at scale.You'll also deliver high quality features end-to-end and contribute to system design and code reviews and begin owning components of production systems within a fast paced, AI native engineering environment.Responsibilities and Duties of the Role:Architect, design, and implement production-grade, distributed systems that leverage real-time telemetry signals and AI-driven anomaly detection to optimize the health, resilience, and reliability of the global streaming ecosystem.Define the architectural patterns and scaling strategies for agentic AI systems powered by modern foundation models (e.g., Claude, GPT-4) to enable automated reasoning and predictive modeling for real-time system health and reliabilityDevelop end-to-end data and decisioning pipelines that transform telemetry, logs, and user signals into actionable insights, automated detection, and root cause analysisCreate and deploy scalable APIs and services that deliver predictive signals, explainability, and insights to engineering teams, operational tools, and product stakeholdersPartner cross-functionally to embed intelligence into workflows (incident response, release validation, subscriber experience insights), improving speed and reducing operational overheadDrive innovation in observability, reliability, and developer productivity through applied AI and new approachesDefine and drive the team's engineering standards and best practices when it comes to clean, well-tested code, thorough code reviews, and mature CI/CD, owning components of production systems and mentor junior engineersRequired Education, Experience/Skills/Training:7+ years of applicable experience in backend development, including building AI-powered or data driven applications and scalable APIs (e.g. FastAPI, Flask)Proficiency in Spark, Pyspark, Python or similar programming languageExperience implementing production-grade systems at scale within a fast-paced, distributed environment.Strong AI/ML engineering experience, including orchestrating foundation models (e.g. Claude, OpenAI, Qwen) using frameworks like LangChain or LangGraph.Familiarity with developing, training, or fine-tuning models using frameworks like PyTorch or TensorFlow.Proficiency with AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate engineering velocity.Experience with modern development practices, including version control (GitHub), containerization (Docker), and cloud-native deployments (AWS/EKS).Strong understanding of API design, microservices architecture, and standard SDLC workflows.Strong analytical and technical skills to troubleshoot issues, perform rapid iteration and quickly come up with possible solutionsStrong collaboration and communication skills, with the ability to work cross-functionally and clearly explain complex technical conceptsPreferred QualificationsExperience with observability platforms (e.g., Datadog, Grafana, Conviva) and handling high-volume telemetry data.Familiarity with large-scale data platforms and distributed data processing tools (e.g., PySpark, Pandas, Databricks, Snowflake).Knowledge of prompt design, model evaluation, and fine-tuning foundation models (GPT-4, Claude).EducationBachelor's degree in Computer Science, Engineering, or equivalent experience