Sr Machine Learning Engineer

Sr Machine Learning Engineer (Req : )

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 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 that powers billions of real-time ad decisions daily across Disney’s video‑on‑demand and live TV properties, including Hulu, Disney+, ESPN, and more.

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.

Success is measured by production outcomes, system reliability, model performance, and continuous iteration based on data and feedback.

• 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

• 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

• 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 Tensor Flow, 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