Software Engineer II

Disney Disney · Media · Glendale, CA +4

Software Engineer II role focused on designing and building AI-driven systems for observability and reliability in Disney's streaming ecosystem. The role involves developing agentic systems, machine learning models, and real-time pipelines to automate issue detection and root cause analysis. Key responsibilities include building AI capabilities, developing data pipelines, creating scalable APIs, and embedding intelligence into operational workflows.

What you'd actually do

  1. Design and operate intelligent, production-grade systems that leverage real-time signals and AI-driven detection to improve the health of streaming platforms, critical services, and customer experience
  2. Build and scale AI-driven capabilities including agentic AI systems powered by modern foundation models (e.g. Claude Opus/Sonnet, GPT-4) enabling automated reasoning and decisioning, as well as predictive modeling and anomaly detection for real-time system health and reliability
  3. Develop end-to-end data and decisioning pipelines that transform telemetry, logs, and user signals into actionable insights, automated detection, and root cause analysis
  4. Create and deploy scalable APIs and services that deliver predictive signals, explainability, and insights to engineering teams, operational tools, and product stakeholders
  5. Partner cross-functionally to embed intelligence into workflows (incident response, release validation, customer insights), improving speed and reducing operational overhead

Skills

Required

  • backend development
  • building AI-powered or data driven applications
  • scalable APIs
  • Agentic Workflows: Orchestrating foundation models (GPT-4, Claude) using frameworks like LangChain or LangGraph
  • Traditional ML: Developing, training, or fine-tuning models using frameworks like PyTorch or TensorFlow
  • AI-assisted development tools (e.g., Cursor, Claude Code)
  • modern development practices
  • version control (GitHub)
  • containerization (Docker)
  • cloud-native deployments (AWS/EKS)
  • API design
  • microservices architecture
  • standard SDLC workflows
  • analytical and technical skills
  • collaboration and communication skills

Nice to have

  • observability platforms (e.g., Datadog, Grafana, Conviva)
  • handling high-volume telemetry

What the JD emphasized

  • AI driven systems
  • agentic systems
  • machine learning models
  • real-time pipelines
  • autonomous agents
  • AI native engineering environment
  • agentic AI systems
  • foundation models
  • predictive modeling
  • anomaly detection
  • decisioning pipelines
  • automated detection
  • root cause analysis
  • predictive signals
  • applied AI

Other signals

  • design and build intelligent, AI driven systems
  • develop agentic systems, machine learning models, and real-time pipelines
  • autonomous agents capable of reasoning over complex system behavior
  • AI native engineering environment