Technology is at the heart of Disney’s past, present, and future. 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.
What You’ll Do
- Develop, optimize, and productionize innovative technologies in generative AI (mixed media, video, and agentic LLM applications) as well as in traditional ML modeling applications.
- Create, evaluate, improve, and optimize technologies.
- Drive innovation and apply state‑of‑the‑art AI and machine learning across advertising domains, including inventory forecasting, ad experience, ad pacing, pricing, targeting, and efficient ad delivery.
- Invent and iterate on novel solutions to complex advertising challenges with rapid prototyping and deployment cycles.
- Design, build, and scale robust ML systems that power core ad platform capabilities.
- Champion engineering excellence through best practices in code quality, system design, and operational reliability.
- Mentor and support junior engineers, fostering a culture of continuous learning and technical growth.
What to Bring
- Bachelor’s in computer science or equivalent experience.
- Prior experience rigorously developing, researching, and/or productionizing generative AI modeling or AI‑based editing domains such as image, video, mixed media, audio, LLMs, or agentic flows (e.g., diffusion models, flow models).
- Very strong interest to self‑teach via publications and training resources in generative modeling, especially generative video and diffusion modeling.
- Experience creating ML datasets, especially in computer vision or generative AI, or developing rigorous quality evaluation processes or data labeling processes. Must include an appreciation for the importance of rigorous quality evaluation processes.
- Experience developing language‑processing applications via LLMs or agentic flows.
- Experience in rapid creative prototyping with generative AI (e.g., research labs, hackathons).
- Minimum 7 years of hands‑on experience developing and deploying large‑scale machine learning systems.
- Strong knowledge of AI/ML technologies, mathematics, and statistics.
- Excellent communication, collaboration skills, and a strong teamwork ethic with both technical and non‑technical audiences.
- Strong foundations in algorithms, data structures, and numerical optimization with experience in programming languages such as Python (primary), Java, and SQL.
- Familiarity with deep learning tools and frameworks such as TensorFlow, PyTorch, JAX, and Hugging Face libraries.
- Expert knowledge with traditional (tabular) ML modeling and methods.
- Proven proficiency in deep learning methodologies, fine‑tuning, and transformer architectures.
- A proven track record of thriving in a fast‑paced, data‑driven, and collaborative work environment.
- Experience working closely with UX and front‑end designers building production generative AI products.
- MS or PhD (preferred) in computer science or equivalent experience.
- Experience with multimodal models and embedding techniques.
- Computer vision or visual content understanding experience.
- Experience in digital video advertising or digital marketing domain.
- Diffusion model or generative AI controls research experience.
Location: Seattle / Santa Monica / Glendale
The hiring range for this position in Santa Monica, CA is $171,600 to $230,100 per year and in Seattle is $179,700 to $241,000. The base pay actually offered will take into account internal equity and 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.
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