Sr Machine Learning Engineer

Senior Machine Learning Engineer Disney Entertainment and ESPN Product & Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for Disney's media business globally. The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses. We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company's media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world. The Ad Platform Engineering organization within Disney Entertainment and ESPN Product & Technology is responsible for building, enhancing, and operating a high-performance, distributed, microservice-based digital advertising platform. This platform powers billions of real-time ad decisions daily across Disney's video-on-demand and live TV properties, including Hulu, Disney+, ESPN, and more. Within Ad Platform Engineering, the Programmatic teams build and maintain Disney's programmatic advertising suite of products and services that comprise Disney's Real-time Ad Exchange (DRAX). DRAX is an award-winning, proprietary supply-side platform (SSP) that enables programmatic deal configuration and integrates demand from multiple third-party sources into Disney's ad server in real time. As a Senior Machine Learning Engineer, you will design, build, and operate production machine learning systems that directly impact revenue, efficiency, and viewer experience at global scale. This is a hands-on, production-focused role, ideal for an experienced ML engineer who enjoys owning complex systems end-to-end, partnering closely with product and engineering teams, and delivering measurable impact in low-latency, high-throughput environments operating at billion-request-per-day scale. This role is not research-only. Success is measured by production outcomes, system reliability, model performance, and continuous iteration based on data and feedback. Daily, you should bring: Strong technical ownership and accountability for production ML systems Effective collaboration and communication across engineering, product, and data partners Comfort operating in ambiguity and translating loosely defined problems into scalable solutions A continuous improvement mindset with attention to performance, reliability, and cost The ability to define and use technical and operational metrics to measure system and model health Responsibilities: Apply modern machine learning techniques to advertising use cases such as inventory forecasting, pricing, targeting, and efficient ad delivery Design, implement, and iterate on ML solutions from experimentation through production deployment and ongoing optimization Build and scale ML architectures that balance model quality, latency, throughput, reliability, and cost Design and maintain feature pipelines and feature stores supporting both real-time inference and offline training Own major components of the model lifecycle, including experimentation, validation, deployment, monitoring, and iteration Analyze experimental results and partner with product and engineering stakeholders to support data-informed decisions Ensure models are observable, debuggable, and explainable in production environments Implement monitoring for model performance, drift, bias, and overall system health Contribute to engineering excellence through high-quality code, sound system design, and operational best practices Provide technical guidance through code reviews, design discussions, and knowledge sharing Basic Qualifications: Bachelor's degree in Computer science or related field of study 5+ years of software engineering experience Minimum 3 years of hands-on experience developing and deploying machine learning systems in production Strong knowledge of machine learning fundamentals, mathematics, and statistics Experience operating ML systems in low-latency, high-throughput environments Strong communication and collaboration skills with both technical and non-technical partners Solid foundations in algorithms, data structures, and numerical optimization Proficiency in Python (primary), with experience in Java and SQL Experience with modern ML frameworks and tooling such as TensorFlow, PyTorch, and Hugging Face Experience with one or more of the following: Deep learning methodologies (e.g., sequence-based or representation learning models) Transformer architectures (e.g., BERT, GPT, ViT) for NLP and/or vision Multimodal embedding techniques across text, image, audio, or structured data Large language models and related evaluation methodologies Retrieval-augmented generation (RAG) architectures Experience building systems on cloud-native infrastructure and distributed platforms Proven ability to thrive in a fast-paced, data-driven, and collaborative environment Preferred Qualifications: Experience in digital video advertising or the digital marketing domain Experience with programmatic advertising or real-time bidding platforms MS or PhD (preferred) in Computer Science or equivalent practical experience The hiring range for this position in Glendale, California is $141,900 to $190,300 per year, Santa Monica, California is $141,900 to $190,300 per year, and Seattle, WA is $148,700 to $199,400 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.