Sr 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.
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Job Summary (Describe what the person will do in the role - how he/she will impact the organization. )

ESPN is investing in next-generation sports personalization and recommendation experiences across its digital products. We are seeking a Senior Machine Learning Engineer who will focus on the design, development, and productionization of advanced deep learning models for large-scale recommendation and ranking systems.

This is a model-focused engineering role. The successful candidate will bring strong foundations in deep learning theory and modern recommender-system architectures, together with the software engineering depth to translate research and prototypes into reliable, production-grade, performance-optimized models. The role will own model architecture decisions, training methodology, offline evaluation, inference optimization, and the model code required for dependable online serving, while partnering with platform and infrastructure teams on deployment and operations.

Responsibilities and Duties of the Role: (Summarize job responsibilities, core deliverables and major duties. What is required for the position to exist? )

• Lead the design and implementation of advanced deep learning models for retrieval, ranking, and personalization, including embedding-based, sequence, transformer, and multi-task architectures.
• Translate product and personalization objectives into model formulations, loss functions, sampling strategies, feature representations, and evaluation plans grounded in machine learning theory.
• Develop production-grade training and inference code with strong standards for correctness, modularity, testing, reproducibility, and maintainability.
• Optimize models for online performance, including latency, throughput, memory efficiency, and cost, using techniques such as distillation, quantization, pruning, batching, compilation, and efficient architecture design where appropriate.
• Own the model productionization path from prototype through offline validation and online integration, ensuring consistency between training and inference behavior.
• Design rigorous offline experiments, ablation studies, and evaluation frameworks; partner with data science and product teams to validate business impact through online experiments.
• Diagnose model behavior and failure modes, including bias, calibration, cold start, feedback loops, delayed labels, distribution shift, and relevance-quality tradeoffs.
• Partner with MLOps, platform, data, and serving teams to define model interfaces, runtime requirements, observability signals, and safe release criteria without owning general ML infrastructure.
• Contribute to technical design reviews, establish model engineering standards, and mentor engineers on deep learning, recommender systems, and production-quality model development.

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

• Strong theoretical and applied knowledge of deep learning, including optimization, representation learning, regularization, generalization, and modern neural network architectures.
• Demonstrated experience designing and launching deep learning models in production, with ownership extending beyond notebook experimentation to production-quality model implementation.
• Hands-on experience with recommendation, ranking, retrieval, search, or personalization models at meaningful scale.
• Expert proficiency in PyTorch, TensorFlow, or a comparable deep learning framework, with strong Python software engineering skills.
• Experience improving inference performance and efficiency, with a practical understanding of latency, throughput, memory, hardware utilization, and model-quality tradeoffs.
• Experience with large-scale model training, distributed data processing, and production model evaluation.
• Solid understanding of experimentation and model evaluation, including metric selection, offline-online correlation, ablation analysis, and statistical validation.
• Ability to communicate complex modeling decisions clearly and collaborate effectively with product, data science, data engineering, platform, and infrastructure partners.

Preferred Qualifications

• Experience with multi-stage recommender systems, including candidate generation, retrieval, ranking, re-ranking, and embedding-based approaches.
• Deep expertise in one or more of the following: transformers, sequence modeling, multi-task learning, graph neural networks, self-supervised learning, contextual bandits, or reinforcement learning for recommendations.
• Experience optimizing neural models with ONNX, TensorRT, CUDA, custom kernels, or comparable inference acceleration technologies.
• Experience designing models under strict real-time serving constraints and measuring p95/p99 latency, throughput, memory footprint, and cost-to-serve.
• Knowledge of recommender-system challenges such as implicit feedback, negative sampling, selection bias, cold start, exploration versus exploitation, and delayed outcomes.
• Research publications, patents, open-source contributions, or equivalent evidence of depth in deep learning or recommender systems.
• Experience operating in fast-paced or 0-to-1 product development environments with ambiguous requirements.

Experience with:

• 5+ years of hands-on experience developing machine learning systems, including substantial experience designing and productionizing deep learning models.

Required Education 

●       Bachelor’s or Master degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.

PAY TRANSPARENCY

The hiring range for this position in New York, NY is $150,900- $202,400 and for Glendale, CA is $144,100 - $193,200. 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.

This position is with Disney Entertainment & Sports LLC, which is part of a business we call Disney Entertainment and ESPN Product & Technology.

To learn more about Disney's guidelines on the use of AI for candidates, click here.

Disney Entertainment & Sports LLC is an equal opportunity employer. Applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability or protected veteran status. Disney champions a business environment where ideas and decisions from all people help us grow, innovate, create the best stories and be relevant in a constantly evolving world.

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