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# Sr Machine Learning Engineer

- **Company:** The Walt Disney Company
- **Location:** Glendale, CA, United States
- **Experience:** Expert
- **Salary:** $150,900.0 - $202,400.0
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Encodings, Nvidia CUDA, Information Engineering, Distributed Computing Environment, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, Software Technical Review, Reinforcement Learning, Graph Neural Networks, Pytorch, Deep Learning, Model Validation, Discretization, Information Technology, Low Latency, ONNX (Open Neural Network Exchange) Format, Free and Open-Source Software, Machine Learning Operations, TensorRT, Feature Extraction
- **Published:** October 21, 2026
- **Apply:** https://dejobs.org/x/x/E8CDB2C310E448CD9F6CA5AA4C8FFD76/job/

## About the Role

* 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.

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.

## Description

* 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.