Sr Machine Learning Engineer

Senior Machine Learning Engineer

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:

• 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:

• 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.

• 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.

• 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.