Senior Machine Learning Engineer (Deep Learning)

Location: New York, New York, United States or Glendale, California, United States

Salary: $150,900–$202,400 in New York, NY; $144,100–$193,200 in Glendale, CA. Bonus and/or long-term incentive units may also be provided.

Company

Disney Entertainment and ESPN Product & Technology builds technology, products, and platforms supporting Disney's media, advertising, distribution, and consumer experiences.

What you will do

• Lead the design and implementation of 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.
• Develop production-grade training and inference code with strong standards for correctness, testing, reproducibility, modularity, and maintainability.
• Optimize models for latency, throughput, memory efficiency, hardware utilization, and cost using distillation, quantization, pruning, batching, compilation, and efficient architectures.
• Own model productionization from prototype through offline validation and online integration, maintaining consistency between training and inference.
• Design experiments and evaluation frameworks, diagnose model failure modes, and collaborate with product, data science, MLOps, platform, data, and serving teams on deployment, observability, and safe releases.

Requirements

• 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, 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, plus strong Python software engineering skills.
• Experience improving inference performance and efficiency, with practical knowledge of latency, throughput, memory, hardware utilization, and model-quality tradeoffs.
• Experience with large-scale model training, distributed data processing, production model evaluation, experimentation, metric selection, offline-online correlation, ablation analysis, and statistical validation.
• Ability to communicate complex modeling decisions and collaborate with product, data science, data engineering, platform, and infrastructure partners.
• 5+ years of hands-on experience developing machine learning systems, including substantial experience designing and productionizing deep learning models.
• Bachelor's or Master's degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.

Nice to have

• Experience with multi-stage recommender systems, including candidate generation, retrieval, ranking, re-ranking, and embedding-based approaches.
• Expertise in transformers, sequence modeling, multi-task learning, graph neural networks, self-supervised learning, contextual bandits, or reinforcement learning for recommendations.
• Experience 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 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 in fast-paced or 0-to-1 product development environments with ambiguous requirements.

Culture & Benefits

• Work on next-generation sports personalization and recommendation experiences across ESPN's digital products.
• Build products and platforms serving millions of people globally across Disney's media portfolio.
• Collaborate across engineering, product, data science, data engineering, platform, infrastructure, and MLOps teams.
• Medical, financial, and other benefits may be provided as part of the compensation package.