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About This Role
Department Description:
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.
Team Description:
We are the Finance Engineering & AI team, and we exist to be Finance's technical partner — for FP&A, Payroll, Controllership, and Tax & Treasury alike — turning business process needs into working software and intelligent automation. Our AI Platform team designs and builds the intelligent automation layer that streamlines and scales finance workflows, while our Business Process Applications team owns and operates the core systems finance relies on every day. Together, we combine deep finance domain expertise with custom engineering to give Finance both a stable operational backbone and a path toward an increasingly automated future.
What You’ll Do:
Disney Financial Insights (D-Fi) is a new platform reimagining how Finance teams and budget owners across Disney interact with financial data — replacing manual pulls, static reports, and email-driven approvals with one AI-powered, orchestrated experience built on top of Disney financial systems.
We're hiring a Senior Manager, AI & Machine Learning, as the founding engineering leader for this AI framework: a hands-on manager and technical lead who will build and run a global engineering team based in Buenos Aires AR, delivering the AI/ML layer, the intelligent data foundation, software workflows/orchestration, and natural-language experience that sits at the center of D-Fi.
This is a build-from-scratch mandate — you'll be shaping team structure, engineering practices, and technical architecture from an early stage (MVP in progress), not inheriting a mature system.
This role is the primary technical point of contact for finance stakeholders & cross-functional partners and is accountable for translating the platform roadmap into a shipped, governed, production-grade system.
As the Senior Manager, AI & Machine Learning, you'll be setting architecture, reviewing design decisions, and staying hands-on enough to credibly evaluate trade-offs your team brings you. This role is also expected to be hands-on — stepping in on implementation, unblocking technical issues in real time, and reviewing code with the depth that comes from doing the work yourself, not just overseeing it.
- Own technical direction end-to-end. Set architecture and technical strategy for the AI/ML and data layers underpinning D-Fi, ensuring every capability — variance analysis, scenario modeling, the natural-language experience — is built on a governed, semantic data foundation rather than one-off pipelines.
- Be the stakeholder-facing technical lead. Act as the primary engineering liaison to finance business partners and leadership — translating challenging business needs into a scoped, buildable technical roadmap, and representing engineering trade-offs credibly to technical and non-technical partners and leaders.
- Own the AI/LLM roadmap. This is the highest-visibility and most sensitive capability on the platform — you'll guide its direction end-to-end to build and maintain trust in AI-generated financial answers.
- Enforce governance by design. Ensure role-based access control (RBAC) is enforced at the data model level — not the application level — across all personas (Finance, Budget Owners, and any future personas), so speed and automation never come at the cost of data governance.
- Set the bar for engineering quality and delivery. Establish practices for code review, testing, model evaluation, and release management across a geo diverse team and multiple disciplines.
- Own the build vs. integrate decisions. Guide when the team builds custom capability versus integrates with existing systems (SAP, Cognos, EPM, Data Marketplace, NetDocs, Coupa, Clarity)
- Champion adoption broadly. Partner with the business finance transformation leads on driving adoption across the enterprise — both finance workers and budget owners.
Required Qualifications & Skills:
- 10+ years of software and/or ML engineering leadership experience, with a strong track record of owning technical direction and architecture for complex systems end-to-end.
- Demonstrated experience architecting and shipping production LLM/AI applications — not just prototypes.
- Production experience with LLM application architecture — RAG pipelines, prompt engineering, and context/retrieval design at scale
- Working knowledge of LLM orchestration frameworks (e.g., LangChain, LlamaIndex, or equivalent) and vector databases/retrieval stores (e.g., Pinecone, Weaviate, pgvector, or Snowflake Cortex Search)
- Strong data modeling background, ideally including semantic/governed data layers (not just raw pipelines)
- Ability to read, write and review Python code
- Working knowledge of modern web/full-stack language or framework sufficient to guide UI-layer architecture decisions
- Familiarity with MLOps practices — model monitoring, retraining pipeline design, containerization, and CI/CD for ML systems
- Experience working across distributed, cross-timezone engineering teams, with the judgment to know what belongs in synchronous vs. asynchronous workflows.
- Comfort operating as the primary technical voice in front of senior business stakeholders — able to translate finance/business problems into technical scope, and technical trade-offs into business language.
Preferred Qualifications:
- Background building internal tools for finance organizations is a plus, but not required — genuine curiosity about the domain is.
- Exposure to ERP/EPM platforms such as SAP, Oracle EPM, or Cognos, and/or experience integrating custom applications against enterprise system APIs
- Experience working alongside or evaluating third-party/consulting-delivered engineering work, with the judgment to assess technical quality and architectural fit quickly
Required Education:
- Bachelor's degree in Computer Science, Engineering, or related field; advanced degree a plus but not required given equivalent experience.
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The hiring range for this position in Orlando, FL is $197,600-$264,900 and in Burbank, CA is $207,400-$278,100 and in Seattle, WA is $217,300-$291,500. 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 $197K-$291K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% 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 $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($244K) sits 15% above the category median. Disclosed range: $197K to $291K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
The Walt Disney Company AI Hiring
The Walt Disney Company has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Orlando, FL, US, Burbank, CA, US. Compensation range: $260K - $291K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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
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