Interested in this AI/ML Engineer role at The Walt Disney Company?
Apply Now →Skills & Technologies
About This Role
Do you thrive on transforming brilliant and complex science into robust, scalable software? Are you driven to advance the platforms and tools that empower scientists to do their best work, faster? Are you energized about building the capabilities that allow data scientists to move from "proof\-of\-concept" to "global production" with the push of a button? We are looking for a visionary leader to bridge the gap between world\-class decision science and industrial\-scale engineering.
The Disney Decision Science and Integration (DDSI) team is the engine behind science\-driven decision\-making across The Walt Disney Company . We leverage advanced algorithms and scientific approaches such as optimization, machine learning, simulation, statistical modeling, genAI and beyond (“decision science”) within innovative software as a service (SaaS) products that shape business decisions across The Walt Disney Company. We support client areas including Disney Entertainment (ABC, The Walt Disney Studios, Disney\+, Hulu, ESPN), Disney Experiences (Theme Parks, Cruise Line, Consumer Products, DVC), Corporate Finance, and others, with strategic applications that enable science\-driven decision\-making and drive business value.
Team Description:
As the Director, Decision Science AI/ML Engineering \& Ops, you will be the architect of our "Science Factory," ensuring our ensemble models and custom algorithms are scalable, observable, and resilient. You will lead the core function that productionizes decision science within DDSI for efficient and effective deployment into SaaS products. This is a foundational leadership role responsible for building the technical backbone to support our next\-generation, AI\-powered products. You will form and mentor a specialized team of AI/ML engineers to create a robust, automated, and scalable factory for deploying our portfolio of ensembled science models and custom algorithms. You will treat AI/MLOps as a product, providing Disney’s decision scientists with the building blocks, feature stores, and automated pipelines they need to innovate at scale. Working hand\-in\-hand with decision scientists, your mission is to increase the speed\-to\-market and reusability of the integrated algorithms that turn data into recommendations via models developed and coded by scientists. You and your team will create advanced tools to empower our scientists \& expert modelers with configurable building\-blocks, automated capabilities, automated testing \& monitoring, and streamlined AI/MLOps processes \- all while fostering an AI\-powered engineering culture to accelerate innovation and push the envelope on both speed\-to\-market and model sophistication \& consumability. In other words, you will lead a specialized team dedicated to leveling\-up the speed to market of decision science, and ensuring our scientists are supercharged with repeatable creation via automation and reusable components. Your goal is to eliminate the friction between model development and deployment. The role will not only be working on greenfield AI initiatives but also comprises stewardship towards maintenance of existing complex ecosystem of production systems.
What You’ll Do:
- Team Vision: Develop and keep relevant a vision for team in a fast\-paced, complex and evolving arena. Foster a high\-performing team of AI/ML engineers and drive a culture of excellence, innovation, and deep collaboration with the science organization and all partner teams.
- MLOps Strategy \& Capability Oversight: Define and execute a comprehensive MLOps roadmap. Architect and implement repeatable and common practices across portfolio of projects, including but not limited to automated model sustainment \& monitoring, highly interoperable and configurable science packages and/or agents, feature stores, and governance required to support complex, ensembled, and algorithm\-driven systems.
- Strategic Leadership: Manage a high\-performing team in a matrixed environment. You will act as the “technical translator” between the Science development teams and the DS Technology organization to ensure our AI/ML services are interoperable with DDSI’s infrastructure, as it continue to evolve in the context of changing toolsets in an AI environment. Define and evolve the AI/ML engineering skill mix, career paths, and hiring strategy required to support DDSI’s long\-term science\-to\-production vision.
- Reusable Building Blocks Creation: Design, build, and champion a library of highly configurable and reusable building blocks (e.g., feature engineering modules, model templates, etc) for scientist and modelers to use, accelerating their model development cycle and reducing time\-to\-production.
- Design Pattern Definitions: Develop roadmaps for reusable capabilities, tools, and agents to harmonize with the portfolio milestones \& deliverables while simultaneously raising the bar on standard expectations for deployed algorithms, including automated metrics and validation, user\-algorithm interactions, and standard features for robust algorithmic guardrails and adaptive\-yet\-stable solution design.
- Productization \& Service Design: Partner directly with Decision Science Delivery team co\-design and engineer scalable batch and/or callable science services for ensembled models and custom algorithms.
- Operational Excellence: Champion the adoption of a portfolio\-wide metrics process to increase visibility of KPIs including batch performance, data quality, model reliability/decision integrity, etc., enabled by the development and implementation of common tools and reusable packages across the portfolio that automate metric capture. Establish a "Production First" culture. Implement rigorous automated testing, validation suites for algorithmic guardrails, and KPI dashboards that track the health of models in the wild.
- Technical Debt \& Modernization: Proactively identify and remediate technical debt within the ML pipelines. You will balance the "velocity of new features" with the "stability of the core," ensuring that our internal SaaS products remain modern, patchable, and secure.
- System Maintenance Stewardship \& Operational Reliability: Collaborate with decision scientists in rapid response to batch process failures and service outages, ensuring internal business partners face minimal disruption. Drive culture and build systems to identify why a system failed—whether due to data drift, pipeline bottlenecks, or algorithmic edge cases—implement permanent fixes, and oversee the technical recovery of production environments, balancing the need for speed with the integrity of the underlying science. Ensure capabilities to drive model output explainability embedded by design for all deployed solutions.
- Champion AI\-Powered Productivity: Foster a culture of innovation by leading the adoption of AI tools within the development process (e.g., code assistants, automated testing) to enhance team efficiency, code quality, and speed. Ensure AI/MLE \& Ops team supports scientists and product teams with process \& tool adoption via documentation and training for reusable building blocks.
- Cross\-Functional Partnership: Serve as the primary partner for Decision Science Delivery team on all aspects of model \& algorithm productization. Collaborate closely with the Directors of Decision Science Technology to ensure seamless integration and deployment of AI/ML services. Partner closely across functional areas to lead directly and via collaboration in a matrixed environment, with emphasis on strong communication, interpersonal collaboration and change management skills
- Demand Management \& Portfolio Prioritization: Establish intake and prioritization mechanisms that maximize reuse, standardization, and enterprise value across the decision science portfolio.
- Change Management: Connect business partners, clients and team with processes improvements and the adoption of the latest business, science and technology standards and best practices
- Stewardship: Ensure all AI/ML platforms and services are designed with security, privacy, explainability, and Responsible AI principles embedded by default. Partner with appropriate teams to ensure compliance with enterprise and regulatory standards. Ensure cost\-aware design of AI/ML capabilities, balancing experimentation velocity with sustainable cloud and compute economics. Partner with teams to ensure responsible scaling of AI/ML/science workloads.
- Communication Agility \& Influence: Ability to operate at all levels of the organization, including tactical project leadership, strategic planning, and business\-focused consulting with clients and executives at all levels. Demonstrated interpersonal skills, with ability collaborate effectively with colleagues ranging from entry\-level professionals to high\-level executives
Required Qualifications \& Skills:
- 12\+ years of related experience
- Prior experience leading decision scientists and/or machine learning engineers to deploy production solutions
- Sufficient statistical and modeling fluency to partner effectively with decision scientists — including the ability to reason about model behavior, diagnose drift or degradation, and assess output integrity in production environments
- Experience with analytical coding languages such as Python, R, SQL
- Experience designing and implementing complex algorithms within constraints for performance, time\-to\-market, and adoptability
- Experience with a breadth of mathematical modeling approaches, including but not limited to supervised learning, unsupervised learning, reinforcement learning, forecasting, estimation, optimization and/or simulation techniques
- Ability to learn technical methods and tools independently
- Strength in leadership to navigate complex organizational dynamics, remove barriers, and be a thought partner for all levels
- Experience with software development tools (e.g. GitLab/GitHub, Docker, CI/CD practices, etc.)
Preferred Qualifications:
- Experience with genAI capability development (e.g., not just AI to develop, but developing AI)
- Cloud computing concepts including auto\-scaling, AWS infrastructure \& services
- Familiarity with emergent design patterns including agent\-driven solutions, interactive LLM/genAI implementations, and beyond
Required Education:
- Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study and/or equivalent work experience
Preferred Education:
- Master’s degree in Computer Science, Computer Engineering, or related discipline, or MBA
\#DISNEYTECH
\#DisneyAnalytics
The hiring range for this position in Orlando, FL is $217,800 to $292,100 per year and in Burbank, CA is $228,700 to $306,700 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 $217K-$306K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($262K) sits 45% above the category median. Disclosed range: $217K to $306K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
The Walt Disney Company AI Hiring
The Walt Disney Company has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Orlando, FL, US, Burbank, CA, US. Compensation range: $198K - $306K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 median, while Prompt Engineer roles sit at $140,000. 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: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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.