Lead Machine Learning Engineer - Personalization

Job Posting Title:

Lead Machine Learning Engineer - Personalization

Req ID:

10121018

Job Description:

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:

1. 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.

2. 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. 

3. Innovation: We develop and implement groundbreaking products and techniques that shape industry norms and solve complex and distinctive technical problems.

Job Summary:

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.

Our team develops and maintains state-of-the-art recommendation and personalization algorithms that serve hundreds of millions of users across Disney+, Hulu, ABC, and ESPN. As a key member of this team, you will collaborate closely with Engineering, Product, and Data teams to apply advanced machine learning techniques in support of strategic personalization initiatives. This is an Individual Contributor role focused on content recommendations. You will lead the research, development, deployment, and optimization of recommendation and personalization algorithms across product surfaces. You will also play a critical role in aligning technical solutions with stakeholder requirements and expectations, partnering with Product, Engineering, and Editorial teams. Beyond execution, you will help define the roadmap for algorithmic innovation—shaping approaches to feature development and contributing to broader company goals in the personalization and recommendation space.

Responsibilities and Duties of the Role:

• Developing and prototyping state-of-the-art Deep Neural Net algorithms for recommendation systems

• Deliver a conceptual solution into production-level implementation & operation at scale, for global user services

• Quickly learn our complex streaming recommendation systems and deep dive into individual components & systems as well as understand overall framework/architecture

• Identify impactful opportunities to improve our business operations and develop practical solutions and plans to lift our business KPI’s.

• Drive business decisions by data driven and pragmatic approach

• Excellent written and oral communication skills

• Leadership to technically guide a team of engineers and work collaboratively with peers to achieve goals with deadline.

Required Education, Experience/Skills/Training:

Basic Qualifications:

• Strong proficiency in at least one of the following deep learning frameworks, tensorflow, pytorch

• Experiences building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment

• Track record from design to full production of effective recommendation systems.

• Experience with cloud services in a production environment (particularly AWS)

• Understanding of statistical concepts (e.g., hypothesis testing, regression analysis)

• Ability to articulate the usage and behavior of models and algorithms to both technical and non-technical audiences

Preferred qualification:

• MS or PhD in statistics, math, computer science, or related quantitative field

• Production experience with developing content recommendation algorithms at scale and familiar with metadata management, data lineage, and principles of data governance

• Deep understanding in personalization challenges in homepage experience and proven records of developing effective solutions

Experience with:

• 7+ years of experience in developing highly scalable machine learning products

• 7+ years writing production-level, scalable Python codes

Required Education:

• Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience

#DISNEYTECH

The hiring range for this position in New York, NY & Seattle, WA is $172,300-$231,100 per year, in San Francisco, CA is $183,700.00-$246,400.00 per year and in Los Angeles, CA is $164,500.00 to $220,600.00 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.

Job Posting Segment:

Product & Data Engineering

Job Posting Primary Business:

PDE - Engagement Experiences & Platforms

Primary Job Posting Category:

Machine Learning

Employment Type:

Full time

Primary City, State, Region, Postal Code:

San Francisco, CA, USA

Alternate City, State, Region, Postal Code:

Date Posted:

2025-05-15