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About This Role
The Director, AI Enablement & Legal Engineering will partner with attorneys, business partners and technology teams to redesign legal workflows, implement technology-enabled solutions, and create scalable ways of working that improve how legal services are delivered across Legal & Global Affairs.
As a key member of the LIFT team, this role will focus on AI-enabled process engineering, workflow modernization, automation, knowledge enablement, and practical adoption of new tools and ways of working. The role will help translate legal practice needs into repeatable processes, operating models, and solutions that improve efficiency, consistency, scalability, and business impact.
Key Responsibilities
Architect and Deploy AI Solutions
- Keeps legal data accurate, well-organized, and responsibly used, treating it as the foundation AI capability is built on and improves alongside
- Design end-to-end AI solutions, using LLMs, retrieval-augmented generation, and agentic frameworks, for contract review, legal research, regulatory analysis, and compliance workflows. Build & enhance the underlying document processing pipelines, including intake, parsing, chunking, embedding, and indexing, that turn contracts, playbooks, and regulatory filings into a system that supports fast, intelligent search and analysis
- Hands-on experience using AI-assisted development tools such as Claude Code, Cursor, or GitHub Copilot to build and ship production solutions faster.
- Establish testing, validation, and quality control practices so AI-enabled legal work meets LGA's standards for accuracy and reliability.
Own Vendor and Technology Stack Strategy
- Evaluate and influence the legal technology portfolio, including build-versus-buy decisions across platforms and specialized providers.
- Track and report the return on investment of AI usage across the department to guide where further investment makes sense.
- Assess external platforms, many of which already run on comparable AI capability, against the case for building in-house and vice-versa. Platform and build decisions should be made with a view toward where AI in Legal is heading, not just its current state.
Build AI Fluency and Legal Engineering Capability
- Design enablement programs calibrated to where each practice area sits on the AI maturity curve, rather than a single training path for everyone.
- Coach and develop talent toward legal engineering skills and help expand the existing network of AI-advanced colleagues across practice areas.
- Measure adoption and effectiveness to guide where further investment in tools or training makes sense.
Partner Across Senior Leadership
- Act as a trusted advisor to senior LGA leaders and cross-functional partners in Technology, Finance, and HR on AI strategy and priorities.
- Translate technical capability into practical, business-relevant recommendations for non-technical stakeholders.
- Navigate a matrixed organization and competing priorities to keep initiatives moving.
Lead with a Service Mindset
- Keep attorneys' and paralegals' daily experience at the center of every solution, so tools solve real friction rather than add process.
- Stay responsive and available to legal teams as they adopt new workflows and tools.
- Build trust through consistent follow-through, not just technical delivery.
Qualifications
Required
- Juris Doctor from an accredited law school and licensed attorney in good standing.
- 10+ years of experience in legal practice, legal operations, legal technology, consulting, transformation, or related fields.
- Demonstrated experience improving legal workflows, processes, service delivery, or operational effectiveness.
- Strong understanding of legal processes, process redesign, legal technology, knowledge management, automation, and workflow tools.
- Experience with AI-enabled solutions, legal technology implementation, workflow platforms, or innovation initiatives.
- Ability to influence and partner effectively with attorneys, senior legal leaders, technology teams, and business stakeholders.
Preferred
- Experience in Legal Operations, legal transformation, legal innovation, consulting, or legal technology organizations.
- Expertise in process engineering, workflow design, Lean, Six Sigma, design thinking, product management, or related methodologies.
- Experience with contract lifecycle management, matter management, legal intake, knowledge management, document automation, or workflow automation platforms.
- Experience helping legal teams adopt AI-enabled solutions, automation, or new ways of working.
The hiring range for this position in California is $185,500-$248,800 per year and in New York is $196,600-$260,600 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 $185K-$260K 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. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($223K) sits 5% above the category median. Disclosed range: $185K to $260K.
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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