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. ESPN is building the next-generation video experience for our global streaming platform, and personalization will be at the core of delivering a world-class user experience. We are seeking a Principal Machine Learning Engineer to serve as the technical architect and driving force behind the design, development, and deployment of our real-time recommendation engine. This is a unique opportunity to lead the technical direction and build foundational personalization capabilities that will directly shape user engagement, satisfaction, and long-term growth. In this role, you will partner closely with engineering, product, data science, and business teams to define system architecture, design large-scale ML solutions, and drive end-to-end ownership of real-time recommendation systems from 0 to 1. You will bring deep technical expertise in recommendation algorithms, real-time serving architectures, and large-scale machine learning systems, as well as the leadership and communication skills to influence cross-functional teams. Responsibilities and Duties of the Role: Serve as the technical architect and primary owner for the design and implementation of ESPN’s real-time short-form video recommendation system.

Design, develop, and deploy large-scale, end-to-end ML pipelines for real-time retrieval, ranking, and personalization at scale.

Lead research, prototyping, and product ionization of cutting-edge recommendation algorithms, leveraging deep learning, embeddings, sequence models, transformers, and multi-task learning.

Define system architecture for low-latency online inference, streaming data pipelines, feature stores, and online/offline model serving.

Collaborate with cross-functional stakeholders to define personalization strategies, system requirements, metrics, and experimentation frameworks to drive continuous improvement.

Lead complex technical discussions and make high-impact design decisions balancing model quality, scalability, system latency, and operational reliability.

Establish ML engineering best practices, development standards, and model governance processes to ensure robust, reliable, and reproducible ML systems.

Mentor and coach other machine learning engineers, helping to grow technical capability across the team and broader organization.

Stay current with state-of-the-art research and industry trends; proactively incorporate emerging technologies into ESPN’s personalization roadmap.

Required Education, Experience/Skills/Training: Basic Qualifications: Proven track record of designing and deploying real-time, large-scale ML recommendation systems (preferably in consumer or streaming platforms).

Strong expertise in machine learning algorithms, deep learning architectures (., sequence models, transformers, embeddings, multi-task learning), and personalization methodologies.

Deep understanding of real-time serving architectures, online inference, feature stores, streaming data pipelines, and low-latency ML systems.

Proficiency in Python and common ML frameworks (., TensorFlow, PyTorch, ONNX), and experience integrating ML models into production services.

Demonstrated technical leadership in cross-functional projects; ability to independently own technical solution design, architecture, and execution in ambiguous 0→1 environments.

Strong communication skills to collaborate with engineering, product, data, and business stakeholders

Preferred qualifications: Experience building short-form video or content-based recommendation systems, including ranking, retrieval, exploration/exploitation, and diversity modeling.

Deep knowledge of real-time personalization challenges such as cold start, feedback loops, delayed labels, and temporal dynamics.

Experience with experimentation platforms (., A/B testing, bandits, reinforcement learning) to drive continuous optimization of recommendation systems.

Experience designing ML systems on cloud platforms (AWS, GCP, Azure) with distributed compute, streaming data, and scalable online serving.

Familiarity with retrieval models, approximate nearest neighbor search, graph-based recommenders, and large-scale embedding management.

Experience collaborating with product and business stakeholders to define personalization goals, metrics, and KPIs.

Strong mentoring capability to help grow and guide a new ML team; prior experience establishing technical standards, ML development best practices, and team capability building.

Prior experience operating in a fast-paced startup or new product incubation environment.

Experience with: 8+ years of hands-on experience building and deploying machine learning models in production environments, with at least 2+ years in recommendation systems or personalization.

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 Los Angeles, CA is $202,900 - $272,100 per year, in San Francisco, CA $222,200 - $297,900 and in New York & Seattle, WA is $212,600 - $285,100 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.