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About This Role
Additional InformationBethesda, MD Pay Range: $151,100\-$250,000 annually Remote Pay Range: $146,000\-$241,000 annually
Job Number26101946
Job CategoryInformation Technology
Location7750 Wisconsin Ave, Bethesda, Maryland, United States, 20814
ScheduleFull Time
Located Remotely?Y
Position Type Management
Bonus Eligible: Y
Stock Package: Y
Expiration Date: 09/10/2026
Position Summary
Marriott International is seeking a strategic and execution\-focused leader to serve as Senior Director, AI Data within the Data Services organization. Reporting to the Managing Vice President, Data Services, this role will lead the design and delivery of the AI\-ready data foundation on which enterprise AI products, analytics, personalization, and intelligent experiences depend.
This leader will be responsible for transforming Marriott data into trusted, contextualized, reusable, and safely consumable assets for both human users and AI systems. The role brings together enterprise semantic models, knowledge graphs, metadata, data products, feature and context services, and governed access patterns that allow AI systems to discover, interpret, and use enterprise information with confidence.
This is a build\-and\-transform mandate within Data Services, requiring strong partnership across Data Platform, Data Engineering, Data Governance, AI/ML, Digital, Loyalty, Operations, Commercial, and corporate functions. The Senior Director will help define a multi\-year roadmap for AI Data and deliver the capabilities sequentially with measurable business impact.
Expected Contributions
Enterprise Ontology \& Semantic Layer
- Define and govern the shared vocabulary of the enterprise so systems, models, analytics, and AI agents operate from consistent definitions of core concepts such as guest, property, stay, transaction, loyalty interaction, reservation, and operational event.
- Lead the development of business\-aligned semantic models and common data definitions that improve interoperability, reuse, and decision consistency across Marriott.
- Partner with domain leaders and governance teams to ensure semantic assets are owned, maintained, and embedded into data products and consumption experiences.
Connected Knowledge Graph
- Move beyond rows\-and\-tables thinking toward a relationship\-first intelligence layer that connects guest signals, property attributes, loyalty behavior, digital interactions, commercial activity, and operational events.
- Lead the design and delivery of knowledge graph capabilities that allow AI systems and analysts to reason across relationships, context, and enterprise entities.
- Prioritize high\-value graph use cases that support personalization, service recovery, operational intelligence, marketing effectiveness, and decision automation.
Agent\-Discoverable Metadata
- Create metadata capabilities that make data assets machine\-readable, discoverable, trusted, and usable by AI systems with appropriate human oversight.
- Expand metadata coverage across business glossary, lineage, freshness, ownership, quality, usage, and access classifications.
- Partner with Data Governance and Data Platform teams to embed metadata as a core service for AI, analytics, and enterprise data consumption.
MCP Servers \& Agent APIs
- Shape governed interfaces through which internal and partnered AI agents can query enterprise knowledge, retrieve context, and trigger approved actions.
- Partner with AI, platform, security, privacy, and architecture teams to establish Model Context Protocol patterns, agent APIs, auditability, and policy controls.
- Ensure AI consumption patterns are designed for scale, transparency, access control, and responsible enterprise use.
Real\-Time Knowledge Movement
- Advance event\-driven and freshness\-aware data capabilities so knowledge assets and downstream AI consumers reflect current reality rather than stale snapshots.
- Partner with engineering and platform teams to reduce batch dependencies where near\-real\-time context is required for AI and business decisioning.
- Drive standards for freshness, observability, reliability, and operational readiness across priority AI Data assets.
AI Data Products \& Context Services
- Establish reusable AI\-ready data products, features, embeddings, vectorized assets, and context services that accelerate delivery of GenAI, ML, personalization, analytics, and agentic solutions.
- Promote domain\-oriented ownership and lifecycle management for data products used by AI and advanced analytics teams.
- Reduce duplication and improve speed\-to\-market by standardizing how context and reusable intelligence assets are created, managed, and consumed.
Candidate Profile
Education and Experience Required
- Bachelor's degree in Computer Science, Information Systems, Engineering, Data Management, Business, or a related field.
- Ten or more years of experience in knowledge engineering, enterprise data, analytics, applied\-AI platforms, data architecture, or related disciplines.
- At least five years of experience owning end\-to\-end delivery of enterprise\-scale data, AI, analytics, or platform capabilities.
- Demonstrable experience designing, delivering, or operating one or more of the following: enterprise ontologies, semantic layers, production knowledge graphs, metadata platforms, feature stores, or real\-time data infrastructure.
- Experience leading cross\-functional teams and complex transformation efforts in a large, matrixed, global organization.
- Working fluency with the agentic\-AI stack, including model context interfaces, retrieval architectures, vector stores, graph stores, metadata\-driven systems, and enterprise governance patterns.
- Experience with AI\-ready data capabilities such as context layers, semantic models, vectorized data assets, feature stores, knowledge graphs, and governed APIs.
Preferred Qualifications
- Strong understanding of data privacy, security, access controls, lineage, data quality, stewardship, and responsible AI practices.
- Proven ability to influence executive stakeholders and communicate with clarity on data strategy, AI readiness, risk, architecture, and business value.
- Comfort operating with senior stakeholders including executives, audit, risk, legal, privacy, owners, franchise partners, and business leaders.
- Track record of hiring, developing, and retaining senior technical, product, and data talent.
- Governed agent access patterns support responsible, auditable, and scalable AI consumption across internal and partnered use cases.
*At Marriott International, we are dedicated to being an equal opportunity employer, welcoming all and providing access to opportunity. We actively foster an environment where the unique backgrounds of our associates are valued and celebrated. Our greatest strength lies in the rich blend of culture, talent, and experiences of our associates. We are committed to non\-discrimination on any protected basis, including disability, veteran status, or other basis protected by applicable law.*
All positions offer a 401(k) plan, stock purchase plan, discounts at Marriott properties, commuter benefits, employee assistance plan, and childcare discounts. Benefits are subject to terms and conditions, which may include rules regarding eligibility, enrollment, waiting period, contribution, benefit limits, election changes, benefit exclusions, and others. Click here to learn more.
Full\-time positions also offer coverage for medical, dental, vision, health care flexible spending account, dependent care flexible spending account, life insurance, disability insurance, accident insurance, adoption expense reimbursements, paid parental leave and educational assistance.
Washington Applicants Only: Employees will accrue paid sick leave, 0\.077 PTO balance for every hour worked and be eligible to receive a minimum of 9 holidays annually.
Marriott HQ is committed to a hybrid work environment that enables associates to Be connected. Headquarters\-based positions are considered hybrid, for candidates within a commuting distance to Bethesda, MD; candidates outside of commuting distance to Bethesda, MD will be considered for Remote positions.
Marriott International is the world’s largest hotel company, with more brands, more hotels and more opportunities for associates to grow and succeed. Be where you can do your best work, begin your purpose, belong to an amazing global team, and become the best version of you.
Salary Context
This $146K-$250K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Marriott International, 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 $214,900 based on 6,420 positions with disclosed compensation. Director-level AI roles across all categories have a median of $274,554. This role's midpoint ($198K) sits 8% below the category median. Disclosed range: $146K to $250K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Marriott International AI Hiring
Marriott International has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Bethesda, MD, US. Compensation range: $250K - $250K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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