Principal Machine Learning Engineer

Disney Entertainment and ESPN Product & Technology 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. Here are a few reasons why we think you’d love working here: Building the future of Disney’s media: Our Technologists are designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come. Reach, Scale & Impact: More than ever, Disney’s technology and products serve as a signature doorway for fans' connections with the company’s brands and stories. Disney+. Hulu. ESPN. ABC. ABC News... and many more. These products and brands – and the unmatched stories, storytellers, and events they carry – matter to millions of people globally. Innovation: We develop and implement groundbreaking products and techniques that shape industry norms, and solve complex and distinctive technical problems. 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. News & Entertainment (N&E) Machine Learning The N&E ML team is responsible for building robust data pipelines and advanced machine learning platforms that deliver personalized experiences to users across Disney's News & Entertainment portfolio including ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios. Our services leverage machine learning models to enable real-time content personalization and targeted distribution across web, mobile, and connected TV platforms, ensuring that users receive the most relevant and engaging content tailored to their interests. Our mission is to drive seamless, resilient, and low‑latency personalized content delivery at scale, while continuously advancing our ML infrastructure and recommendation algorithms across one of the world's most iconic collections of entertainment brands. Job Summary: As a Principal Machine Learning Engineer, you will define and own the technical architecture and strategic direction of the N&E ML Platform across a large, complex problem space spanning Disney's News & Entertainment portfolio. You will drive step‑function improvements in personalization, recommendation systems, and ML infrastructure - not just at the feature level, but across entire product and platform domains. You will serve as a thought leader who bridges business objectives and technical execution, partnering with senior leadership, product, and cross‑org engineering communities to set the standard for ML excellence within News & Entertainment. Your impact will be measured by the quantifiable outcomes you drive for our guests and the durable technical foundations you build for the teams around you. Responsibilities and Duties of the Role: Focus on major areas of work, typically 20% or more of role Architecture Ownership: Define and own the end-to-end architecture of the N&E ML platform across a large problem space spanning ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios. Author architecture documents, drive them through review, and oversee implementation to ensure solutions are scalable, reliable, and aligned with platform‑wide standards. Strategic Technical Leadership: Identify, scope, and prioritize the most impactful and time‑sensitive ML workstreams across the N&E portfolio. Break down and sequence complex initiatives, proactively surface risks to leadership, and drive outcomes with a clear metrics‑driven mindset. ML Platform & Infrastructure: Drive the design and evolution of infrastructure supporting the full ML lifecycle across diverse content types and brands: data pipelines, workflow orchestration, feature stores, batch training, and low‑latency online serving. Champion reliability, quality, and operational excellence across the platform. Innovation & Industry Awareness: Stay at the forefront of industry trends in ML, AI, and data engineering. Proactively identify and champion the adoption of new technologies, frameworks, and patterns that drive improvement across the N&E portfolio (e.g. recommendation systems, LLMs, RAGs, object detection, autogenerated content tagging). Incident & Reliability Ownership: Own and speak to production incidents during weekly meetings with leadership. Hold the team to the right engineering processes and drive a culture of reliability, observability, and continuous improvement across the N&E ML problem space. Cross-Org Engagement: Actively participate in and contribute to the broader Machine Learning community across Disney Entertainment & ESPN. Drive and influence engineering standards, cross‑org programs, and best practices that extend beyond the N&E ML team. Business & Objectives Alignment: Serve as a thought leader who deeply understands the business objectives and problems across the N&E portfolio - not just the technical ones. Lead metrics‑driven programs that connect ML platform investments directly to measurable guest experience and business outcomes across all brands. Mentorship & Culture: Mentor and elevate senior engineers, fostering a culture of ownership, technical rigor, and continuous learning. Be a confident, vocal, and optimistic leader who inspires the team and drives outcomes people want to rally around. Required Education, Experience/Skills/Training: Basic Qualifications Bachelor’s degree in computer science, Information Systems, Statistics, Math, or comparable field of study, and/or equivalent work experience 10+ years of experience building and operating ML engineering systems in production environments, with a track record of owning large, complex problem spaces Deep expertise in data science, deep learning algorithms, and statistical methods applied to real‑world, large‑scale engineering problems Demonstrated experience owning architecture across a significant platform or product domain - including authoring architecture documents, driving reviews, and leading implementation Proven ability to drive quantifiable improvements in ML platform capabilities, personalization quality, or recommendation system performance Experience designing and evolving backend microservices for large‑scale distributed systems using REST Strong expertise with cloud infrastructure, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize) Deep hands‑on experience with big data technologies such as Databricks, Spark, Kinesis, and Kafka Experience leading incident response for high priority incidents and driving reliability programs across a team or platform Active participation in cross‑organizational engineering communities, standards‑setting, and architectural governance Proven track record as a metrics‑driven technical leader who connects engineering decisions to business outcomes Exceptional communication, influence, and collaboration skills — comfortable presenting to and aligning senior leadership and cross‑functional stakeholders Experience working in Agile/Scrum environments with strong prioritization and stakeholder management skills Preferred Qualifications Experience with agentic AI workflows and frameworks (e.g. LangGraph, AutoGen, CrewAI) and applying them to automate complex ML and data engineering tasks Familiarity with AI‑assisted development tools such as Claude, Cursor, or GitHub Copilot to accelerate software development lifecycle and engineering productivity Familiarity with prompt engineering, fine‑tuning, and evaluation frameworks for large language models in production environments Experience with MLOps platforms and modern model lifecycle management tools (e.g. MLflow, SageMaker, Vertex AI) The hiring range for this position in Glendale, California is $207,400 - $278,100 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate’s geographic region, job‑related knowledge, skills, and experience among other factors. A bonus and/or long‑term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered. #J-18808-Ljbffr