Principal Machine Learning Engineer

Job Posting Title:

Principal Machine Learning Engineer

Req ID:

Job Description:

Disney Entertainment and ESPN Product & Technology

Technology is at the heart of Disneys 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 Disneys 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 Companys 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 youd love working here:

Building the future of Disneys 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 Disneys technology and products serve as a

signature doorway for fans connections with the companys brands and stories.

Disney. Hulu. ESPN. ABC. ABC Newsand 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 Disneys 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 worlds 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 Disneys 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

• Bachelors 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 $207400 - $278100 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidates 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.

Job Posting Segment:

Product Engineering

Job Posting Primary Business:

PE - Streaming Backend

Primary Job Posting Category:

Machine Learning

Employment Type:

Full time

Primary City State Region Postal Code:

Glendale CA USA

Alternate City State Region Postal Code:

USA - NY - 7 Hudson Square

Date Posted: