Sr Machine Learning Engineer

Disney Entertainment & Sports LLC is hiring a Sr Machine Learning Engineer for ESPN, focused on building and productionizing deep learning models for large-scale recommendation and ranking.

Responsibilities

• Design and implement 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 such as loss functions, sampling strategies, feature representations, and evaluation plans.
• Build production-grade training and inference code with standards for correctness, modularity, testing, reproducibility, and maintainability.
• Improve online performance including latency, throughput, memory efficiency, and cost, applying techniques such as distillation, quantization, pruning, batching, compilation, and efficient architecture design where appropriate.
• Own the productionization lifecycle from prototype through offline validation to online integration, ensuring training and inference behavior remain consistent.
• Run rigorous offline experiments, including ablation studies and evaluation frameworks; collaborate with data science and product teams to validate impact using online experiments.
• Investigate 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).
• Participate in technical design reviews, help establish model engineering standards, and mentor engineers in 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.
• Proven experience designing and launching deep learning models in production, with ownership beyond notebook work to production-quality implementation.
• Hands-on experience with recommendation, ranking, retrieval, search, or personalization models at meaningful scale.
• Expert proficiency in PyTorch, TensorFlow, or comparable deep learning frameworks, plus strong Python software engineering skills.
• Experience improving inference performance and efficiency, with 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.
• Strong 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 across product, data science, data engineering, platform, and infrastructure partners.
• Experience with multi-stage recommender systems including candidate generation, retrieval, ranking, re-ranking, and embedding-based approaches.
• Deep expertise in one or more: 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.
• Experience designing models for strict real-time serving constraints, 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 working in fast-paced or 0-to-1 environments with ambiguous requirements.
• 5+ years of hands-on experience developing machine learning systems, including substantial experience designing and productionizing deep learning models.
• Bachelor’s or Master degree in Computer Science, Machine Learning, Data Science, or a related field, or equivalent practical experience.

Location & Work Mode

• New York, NY (onsite)

Salary

• USD 144,100 - 202,400 per year

Technologies

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

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 may vary based on candidate geographic region, job-related knowledge, skills, and experience.
• A bonus and/or long-term incentive units may be provided in addition to the full range of medical, financial, and/or other benefits, dependent on level and position.