Interested in this AI/ML Engineer role at The Walt Disney Company?
Apply Now →Skills & Technologies
About This Role
Job ID 10141861 Location Burbank, California, United States Business The Walt Disney Company (Corporate) Date posted Mar. 24, 2026
#### Job Summary:
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.
What You’ll Do:
The Emerging Tech \& Automation team is seeking a forward\-thinking and hands\-on Staff GenAI Engineer to accelerate innovation across the enterprise. This role blends research, engineering, and rapid prototyping to explore the frontiers of Generative AI (GenAI), Large Language Models (LLMs), and automation. You will work closely with product, data, and engineering teams to de\-risk cutting\-edge technologies and build scalable proof\-of\-concepts (PoCs) and Minimum Lovable Products (MLPs) that inform enterprise\-wide transformation. This includes designing and implementing systems leveraging generative models (LLMs, diffusion models, etc.) for content generation and intelligent automation
Key Responsibilities
- Stay current with advancements in machine learning, GenAI, and cloud\-native tooling; Evangelize new technologies and champion adoption through internal demos, knowledge sharing, and documentation.
- Contribute to technical strategy, patent filings, and the development of reusable frameworks that support long\-term innovation.
- Lead design and rapid prototyping of GenAI\-enabled solutions (chatbots, summarizers, copilots) to explore and validate high\-impact business opportunities.
- Evaluate and integrate LLMs and modern GenAI techniques (e.g., RAG, fine\-tuning, prompt engineering, embeddings, vector databases) into enterprise use cases.
- Lead architecture discussions, conduct model performance evaluations, and implement best practices in MLOps and GenAI infrastructure.
- Design and build reusable PoCs, pilots, and platform components leveraging modern GenAI/ML frameworks.
- Work closely with business groups across the enterprise to understand their business objectives, use cases and challenges.
- Embed directly with teams including product managers, researchers, and engineering teams to co\-develop innovative, robust and scalable GenAI solutions—translating context\-rich insights into technical designs and working prototypes that drive meaningful innovation.
- Develop and maintain automation and AI platform components to support scalability, reusability, and experimentation across the organization.
- Mentor junior engineers and foster a culture of experimentation, documentation, and continuous learning.
Required Qualifications \& Skills:
- 7\+ years of experience in software engineering or ML/AI engineering, with 2\+ years hands\-on with GenAI or LLM technologies.
- Strong written and verbal communication skills, including the ability to document findings, craft compelling narratives, and present to executive audiences.
- Hands\-on experience deploying solutions in cloud environments such as AWS, Azure, or GCP.
- Proven experience working on AI/ML, GenAI, automation, or advanced data platform initiatives in enterprise or R\&D settings.
- Advanced proficiency in Python, with strong understanding of system design and scalable architecture.
- Hands\-on experience with ML frameworks such as PyTorch, TensorFlow, HuggingFace Transformers, or LangChain.
- Deep knowledge of GenAI techniques including prompt engineering, RAG pipelines, LLM evaluation and fine\-tuning LLMs.
- Experience designing and building scalable AI/ML systems, including data architecture spanning NoSQL, graph/vector, and relational databases.
- Strong understanding of MLOps best practices and experience with tools like MLflow, Kubeflow, Docker, and Kubernetes.
- Demonstrated ability to collaborate cross\-functionally and influence technical direction across engineering, product, and business teams.
- Must be detail oriented, self\-organized, and capable of simultaneously prioritizing multiple efforts
Preferred Qualifications:
- Experience working in R\&D or innovation\-focused environments with fast\-paced prototyping and ambiguous problem spaces.
- Background in building tools or platforms that enable enterprise\-wide adoption of AI/ML technologies.
- Previous contributions to open source, patents, or publications in GenAI or ML.
- Passion for Disney's mission and values
Education
- Bachelor’s degree in Computer Science, Machine Learning, Data Science, or comparable field of study, and/or equivalent work experience; advanced degree a plus.
Project Hire Information:
This is a 6 month project hire position with no guarantee of permanent placement.
The hiring range for this position in Burbank, CA is $171, 600 to $230,100 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.
Salary Context
This $171K-$230K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At The Walt Disney Company, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $166,983 based on 13,781 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($200K) sits 20% above the category median. Disclosed range: $171K to $230K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
The Walt Disney Company AI Hiring
The Walt Disney Company has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Lake Buena Vista, FL, US, Seattle, WA, US, Celebration, FL, US. Compensation range: $72K - $306K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (2,416) are outnumbered by mid-level (16,247) and senior (5,153) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.