Lead Machine Learning Engineer - News

Job Summary:

Disney Entertainment & ESPN Technology

On any given day at Disney Entertainment & ESPN Technology, we’re reimagining ways to create magical viewing experiences for the world’s most beloved stories while also transforming Disney’s media business for the future. Whether that’s evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize flexibility and efficiency, or delivering Disney’s unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world.

A few reasons why we think you’d love working for Disney Entertainment & ESPN Technology

• Building the future of Disney’s media business: DE&E Technologists are designing and building the infrastructure that will power Disney’s media, advertising, and distribution businesses for years to come.
• Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day – from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.
• Innovation: We develop and execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.

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.

Job Summary:

The News ML team is responsible for building robust data pipelines and advanced machine learning platforms that deliver personalized experiences to users across ABC News, Good Morning America, and local news stations. 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 news 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.

As a Lead Machine Learning Engineer, you will play a leading role in shaping the technical direction of the News ML Platform. You will drive infrastructure for scalable learning, inference, and monitoring, conduct in-depth data exploration and analysis, and collaborate across product, data, and engineering teams to power exceptional, personalized guest experiences. Your work will directly support strategic initiatives to help shape the roadmap for algorithmic innovation while ensuring that solutions are scalable, impactful, and aligned with stakeholder needs.

Responsibilities and Duties of the Role:

• Own complex technical initiatives end-to-end, from technical design through production deployment and operational excellence
• Design and develop infrastructure supporting the full cycle of machine learning, including data pipelines and workflow orchestration, data discovery and quality tools, and feature libraries
• Drive data and ML-driven solutions for diverse engineering use cases such as recommendation systems, object detection, autogenerated tagging solutions, RAGs 
• Partner with product, editorial, and engineering stakeholders to translate business requirements into robust technical solutions
• Strategically prioritize initiatives and technical workstreams to deliver the highest-impact and most time-sensitive outcomes, while proactively identifying, communicating, and mitigating risks to ensure successful execution
• Champion engineering best practices across code quality, testing, CI/CD, observability, and incident response
• Mentor and coach engineers, fostering a culture of ownership, collaboration, and continuous improvement
• Contribute to technical documentation and promote knowledge sharing across teams

Required Education, Experience/Skills/Training:

Minimum and Preferred. Inclusive of Licenses/Certs (include functional experience as well as behavioral attributes and/or leadership capabilities)

Basic Qualifications

• Bachelor’s degree in computer science, Information Systems, Statistics, Math, or comparable field of study, and/or equivalent work experience
• 7+ years of software engineering experience
• 5+ years of hands-on experience developing and deploying machine learning systems in production
• Expertise in data science, deep learning algorithms, or statistical methods to solve real-world engineering problems
• Comfortable operating at all levels of the predictive stack, including data collection, data analysis, feature engineering, batch training and low-latency online serving
• Experience designing and developing backend microservices for large-scale distributed systems using REST
• Experience with cloud infrastructure, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize)
• Familiarity with developing and deploying Spark and ML pipelines
• Hands-on experience with big data technologies such as Databricks, Kinesis, Kafka
• Proven leadership, coaching, and mentoring skills, with the ability to inspire and empower a team towards achieving business goals
• Experience with observability tools for metrics, logging, and monitoring such as Datadog
• Experience working in Agile/Scrum development environments
• Excellent communication skills and a commitment to collaboration in a fast-paced, guest-focused environment

The hiring range for this position in New York, NY is $148,700 - $199,400 per year and in Glendale, CA is $141,900 - $190,300. 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.