Sr Machine Learning Engineer

Disney Entertainment & Sports LLC is seeking a Senior Machine Learning Engineer for ESPN to help build and productionize advanced deep learning models that power large-scale recommendation and ranking experiences. This onsite role in New York, NY will drive key architecture and training decisions, then partner with platform and infrastructure teams to bring models into reliable online serving.

The position focuses on end-to-end ownership across model design, offline evaluation, inference optimization, and online integration, with an emphasis on consistency between training and production behavior.

Responsibilities

• Lead the design and implementation of advanced deep learning models for retrieval, ranking, and personalization, including embedding-based, sequence, transformer, and multi-task architectures.
• Convert product and personalization goals into model formulations, loss functions, sampling strategies, feature representations, and evaluation plans grounded in machine learning theory.
• Build production-grade training and inference code with standards for correctness, modularity, testing, reproducibility, and maintainability.
• Optimize models for online performance, targeting latency, throughput, memory efficiency, and cost, using approaches such as distillation, quantization, pruning, batching, compilation, and efficient architectural design when appropriate.
• Own the productionization path from prototype through offline validation and online integration, ensuring training and inference behavior remain aligned.
• Create rigorous offline experimentation plans, ablation studies, and evaluation frameworks, and collaborate with data science and product teams to validate 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.

Requirements

• Strong theoretical and applied knowledge of deep learning, including optimization, representation learning, regularization, generalization, and modern neural network architectures.
• Experience designing and launching deep learning models in production with ownership that goes beyond notebook experimentation.
• 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, along with strong Python software engineering skills.
• Experience improving inference performance and efficiency, including practical understanding of latency, throughput, memory, hardware utilization, and quality tradeoffs.
• Experience with large-scale training, distributed data processing, and production model evaluation.
• Solid understanding of experimentation and evaluation, including metric selection, offline-online correlation, ablation analysis, and statistical validation.
• Ability to communicate complex modeling decisions clearly and collaborate with product, data science, data engineering, platform, and infrastructure teams.
• Experience with multi-stage recommender systems, including candidate generation, retrieval, ranking, re-ranking, and embedding-based approaches.
• Deep expertise in one or more areas such as 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 including 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 settings with ambiguous requirements.
• 5+ years of hands-on experience developing machine learning systems, including substantial deep learning model design and productionization experience.
• Bachelor’s or Master’s degree in Computer Science, Machine Learning, Data Science, or related field, or equivalent practical experience.

Technologies

• PyTorch
• TensorFlow
• Python
• ONNX
• TensorRT
• CUDA

Compensation and 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 internal equity into account and may vary based on candidate geographic region and job-related knowledge, skills, and experience.
• A bonus and/or long-term incentive units may be provided in addition to medical, financial, and/or other benefits, depending on the level and position offered.