Staff Machine Learning Engineer (AI/ML Engineer)

The Walt Disney Company•Seattle, WA

19h

About The Position

At Disney, we’re storytellers. We make the impossible, possible. The Walt Disney Company (TWDC) is a world-class entertainment and technological leader. Walt’s passion was to continuously envision new ways to move audiences around the world—a passion that remains our touchstone in an enterprise that stretches from theme parks, resorts and a cruise line to sports, news, movies and a variety of other businesses. Uniting each endeavor is a commitment to creating and delivering unforgettable experiences — and we’re constantly looking for new ways to enhance these exciting experiences. The Enterprise Technology mission is to deliver technology solutions that align to business strategies while enabling enterprise efficiency and promoting cross-company collaborative innovation. Our group drives competitive advantage by enhancing our consumer experiences, enabling business growth, and advancing operational excellence. The AI Engineering team builds ML solutions that enable creativity, personalization, and operational excellence across the company. You’ll be part of a team that values innovation, agility, and impact—where your work directly contributes to the future of entertainment. We are seeking a Staff Machine Learning Engineer to lead the development and maintenance of advanced generative AI models and intelligent agents. You will architect core systems utilizing the latest in large language models (LLMs) and agentic frameworks (e.g., LangChain, ADK, Strands), mentor other engineers, and collaborate cross-functionally to deliver transformative products and experiences.

Requirements

7+ years industry experience in machine learning, generative AI, NLP, or agent development.
Deep expertise in Python and modern ML libraries (scikit-learn, TensorFlow, PyTorch).
Demonstrated experience architecting, deploying, and maintaining LLMs and generative AI models.
Hands-on proficiency with agentic frameworks like LangChain, ADK, Strands, or equivalent.
Proven ability designing and orchestrating agentic workflows and multi-agent systems.
Experience developing, deploying, and managing ML solutions on cloud platforms (AWS, GCP, Azure).
Strong collaborator and communicator experienced in leading cross-disciplinary teams.
Familiarity with responsible AI practices, compliance, and security requirements.
Git, Docker, CI/CD, and software best practices.
Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience

Nice To Haves

Record of publications, patents, or open-source contributions related to generative AI, LLMs, or intelligent agents.
Experience integrating agent-based systems into consumer applications at scale.
Background in recommender systems, personalization, or conversational interfaces.
Solid understanding of reinforcement learning, RLHF, or related agent learning paradigms.
Experience with large-scale data pipelines and ML platform engineering.
Advanced degree (Master’s/PhD) in Computer Science, Engineering, Mathematics, or comparable field of study

Responsibilities

Architect, design, and oversee the implementation of advanced ML solutions, including generative models and intelligent agents.
Lead the creation, deployment, and ongoing enhancement of agents using agentic frameworks such as LangChain, ADK, Strands, and emerging technologies.
Build scalable platforms for conversational AI, automation, and personalized guest experiences.
Drive strategy and best practices for prompt engineering, model fine-tuning, orchestration, and agent reliability.
Mentor, coach, and provide technical guidance to junior and mid-level engineers, fostering a culture of innovation and excellence.
Partner with product, engineering, and research teams to define AI/agentic roadmaps and deliver business-impactful solutions.
Lead end-to-end model lifecycle: data acquisition, preprocessing, model selection, training, evaluation, and deployment.
Ensure robust documentation and reproducibility across models, agents, and workflows.
Set standards for safety, privacy, responsibility, and ethical use of AI/agents.
Track and evaluate new trends, tools, and frameworks in the rapidly evolving generative AI and agentic ecosystem.
Present findings, strategies, and technical insights to leadership and stakeholders.