Lead Machine Learning Engineer

Lead Machine Learning Engineer

At Disney Entertainment and ESPN Product & Technology, the Ad Platform Engineering organization builds a high‑performance, distributed, microservice‑based digital advertising platform powering billions of real‑time ad decisions across Disney’s video‑on‑demand and live TV properties. As a Lead Machine Learning Engineer, you will serve as a hands‑on technical leader responsible for delivering high‑impact machine learning systems while guiding technical direction within your domain.

You will design, build, and operate production ML systems at scale, mentor engineers, and partner closely with product and engineering leaders to ensure machine learning solutions are reliable, performant, and aligned with business goals.

• Lead the design and delivery of machine learning solutions across advertising use cases such as inventory forecasting, pricing, targeting, and efficient ad delivery.
• Apply modern machine learning techniques to solve complex, real‑time advertising problems.
• Provide technical leadership for ML system architecture, modeling approaches, and production readiness within your domain.
• Design, build, and scale ML architectures that balance model quality, latency, throughput, reliability, and cost.
• Oversee the full ML lifecycle for owned systems, from experimentation through production deployment and iteration.
• Design and maintain feature pipelines and feature stores supporting both real‑time inference and offline training.
• Partner with product and engineering stakeholders to translate requirements into clear technical plans and measurable outcomes.
• Interpret experimental results and guide data‑informed decision‑making.
• Ensure ML systems are observable, debuggable, and explainable in production.
• Establish and maintain monitoring for model performance, drift, bias, and system health.
• Champion engineering excellence through best practices in code quality, system design, testing, and operational reliability.
• Mentor and support engineers through code reviews, design discussions, and ongoing technical guidance.

• Bachelor’s in Computer Science or equivalent practical experience.
• 7+ years of software engineering experience.
• 5+ 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 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.

• MS or PhD (preferred) in Computer Science or equivalent practical experience.
• Experience in digital video advertising or the digital marketing domain.
• Experience with programmatic advertising or real‑time bidding platforms.

The hiring range for this position in Glendale, California is $171,600 to $230,100 per year, Santa Monica, California is $171,600 to $230,100 per year, and Seattle, WA is $179,700 to $241,000 per year. The base pay is determined by internal equity and may vary based on geographic region, job‑related knowledge, skills, and experience. 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 other benefits, depending on level and position offered.