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Disney Entertainment and ESPN Product & Technology

Product Engineering is a unified team responsible for the engineering of Disney Entertainment & ESPN digital and streaming products and platforms. This includes product engineering, media engineering, quality assurance, engineering behind personalization, commerce, lifecycle, and identity.

The Observability & Insights group ensures that Disney Streaming’s distributed systems are reliable, performant, and transparent. We build the telemetry, dashboards, alerting, insights pipelines, and developer experience tooling that enable engineers across the organization to understand system health and take action quickly.

Job Summary:

As a Software Engineer II on the Reliability Insights team, you will design and build data and reporting pipelines that evaluate and prove the real-world performance, precision, and business impact of Disney’s operational AI and observability tools. You will bridge backend engineering and data-driven insights, transforming raw telemetry, AI agent interactions, and operational logs into clear metrics around AI reliability, model drift, and incident mitigation.  

In this role, you will build automated ground-truth pipelines and model evaluation frameworks that quantify how effectively AI agents and anomaly models perform in production. You will capture end user feedback, correlate AI signals with incident resolution timelines (e.g., Mean Time to Mitigation), and build single pane dashboards for engineering and leadership. Partnering closely with SRE, product, and platform engineering teams, you will eliminate visibility gaps, reduce alert noise, and establish clear standards for AI model performance across Disney+, Hulu, and ESPN.

Responsibilities:

• Design, build, and maintain automated feedback pipelines that capture operational outcomes (confirmed incidents, false positives, dismissed alerts) and correlate them directly back to AI model predictions and agent actions.
• Develop programmatic evaluation frameworks to track model precision, recall, and drift over time, establishing automated threshold alerts to proactively trigger model tuning and prevent alert fatigue.
• Build end-to-end data pipelines linking AI outputs to incident lifecycles, quantifying business impact through metrics such as Service to Incident correlation, notification success rates, and Mean Time to Mitigation (MTTM).
• Design and deploy scalable APIs, metrics layers, and single-pane model health dashboards that present clear AI ROI, operational health, and performance narratives to leadership and technical stakeholders.
• Build mechanisms to capture structured feedback from end users to continuously feed real-world usage signals back into model improvement cycles.

Basic Qualifications:

• 3+ years of applicable experience in backend and data pipeline engineering, including building scalable APIs (e.g., FastAPI, Flask) and metrics/insight generation platforms.
• Strong experience with data platform engineering and distributed processing tools (e.g., PySpark, SQL, Pandas, Databricks, Snowflake) to process and analyze telemetry data.
• Practical understanding of model evaluation concepts, such as precision, recall, model drift, and ground-truth labeling systems.
• Hands-on experience with modern development practices, including version control (GitHub), containerization (Docker), and cloud-native deployments (AWS/EKS).
• Proficiency with AI-assisted development tools (e.g., Cursor, Claude Code) to accelerate engineering velocity.
• Strong analytical skills to translate complex system telemetry and operational logs into meaningful reliability metrics and business insights.
• Excellent cross-functional collaboration and communication skills, with a track record of partnering with operational teams (SRE, DevOps) to turn raw data into actionable insights.

Preferred Qualifications:

• Experience with observability, telemetry, and monitoring platforms (e.g., Datadog, Grafana, Conviva).
• Familiarity with agentic workflows and foundation models (e.g., GPT-4, Claude) and how to evaluate their output accuracy.
• Knowledge of incident management workflows and SRE metrics (e.g., MTTR, MTTD, alert suppression).
• Prior experience working on data attribution, metrics aggregation, or financial/performance visibility tooling in high scale environments.

Education

• Bachelor’s degree in Computer Science, Engineering, or equivalent experience

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