Principal Machine Learning Engineer

Lead the technical direction of Disney’s News and Entertainment machine learning platform, spanning architecture, reliability, and production impact.

Responsibilities

• Own end-to-end ML platform architecture for News and Entertainment across a broad portfolio including ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios; author architecture documents, drive review, and oversee implementation to keep solutions scalable, reliable, and aligned with platform standards.
• Identify and prioritize high-impact ML initiatives across the N&E portfolio; scope workstreams, sequence complex efforts, surface risks to leadership early, and deliver outcomes with a metrics-driven approach.
• Design and evolve ML infrastructure for the full lifecycle across diverse content types and brands, including data pipelines, workflow orchestration, feature stores, batch training, and low-latency online serving; champion reliability, quality, and operational excellence.
• Stay ahead of ML/AI/data engineering trends and champion adoption of new technologies and patterns to improve outcomes across the portfolio, including recommendation systems, LLMs, RAGs, object detection, and autogenerated content tagging.
• Own production incident engagement by speaking to production incidents during weekly leadership meetings; drive appropriate engineering processes and foster a culture of reliability, observability, and continuous improvement.
• Contribute to broader ML community efforts within Disney, including participation across Disney Entertainment & ESPN; influence engineering standards, cross-org programs, and shared best practices beyond the N&E team.
• Connect platform investment to business and guest outcomes by understanding business objectives and problems across the portfolio; lead metrics-driven programs that tie ML improvements to measurable experience and business results.
• Mentor and elevate senior engineers, building a culture of ownership, technical rigor, and continuous learning; act as a visible, collaborative leader focused on outcomes.

Requirements

• Bachelor’s degree in computer science, Information Systems, Statistics, Math, or a comparable field (or equivalent work experience).
• 10+ years building and operating ML engineering systems in production environments, with experience owning large, complex problem spaces.
• Deep expertise in data science, deep learning algorithms, and statistical methods applied to large-scale real-world engineering problems.
• Proven architecture ownership across a significant platform or product domain, including authoring architecture documents, leading reviews, and driving implementation.
• Demonstrated impact driving measurable improvements in ML platform capabilities, personalization quality, or recommendation system performance.
• Backend microservices experience for large-scale distributed systems using REST.
• Strong cloud expertise, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize).
• Hands-on big data experience with Databricks, Spark, Kinesis, and Kafka.
• Incident response leadership for high priority incidents and experience driving reliability programs across a team or platform.
• Active cross-org participation in engineering communities, standards-setting, and architectural governance.
• Metrics-driven technical leadership that connects engineering decisions to business outcomes.
• Exceptional communication and influence with comfort presenting to and aligning senior leadership and cross-functional stakeholders.
• Agile/Scrum experience with prioritization and stakeholder management.

Preferred Qualifications

• Experience with agentic AI workflows and frameworks (e.g., LangGraph, AutoGen, CrewAI) applied to automate complex ML and data engineering tasks.
• Familiarity with AI-assisted development tools such as Claude, Cursor, or GitHub Copilot.
• Familiarity with prompt engineering, fine-tuning, and evaluation frameworks for large language models in production.
• Experience with MLOps and modern model lifecycle management tools (e.g., MLflow, SageMaker, Vertex AI).

Technologies

• REST
• AWS: Step Functions, Lambda, Glue, SQS, SNS, Personalize
• Databricks, Spark, Kinesis, Kafka
• LLMs, RAGs
• Object detection
• LangGraph, AutoGen, CrewAI
• Claude, Cursor
• GitHub Copilot
• MLflow, SageMaker, Vertex AI
• Agile/Scrum

Location: Glendale, CA (onsite)

Compensation: USD 210,500 - 295,900 per year