Interested in this AI/ML Engineer role at Stanford Hotels Corporation?
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Salary Range: $75,000\-$95,000 \- This may fluctuate based on experience or education.
The AI Marketing Optimization Specialist is responsible for leveraging artificial intelligence, search optimization strategies, and digital applications to increase visibility, demand, and bookings across our hotel portfolio. This role blends marketing, IT applications, and AI‑driven analytics to ensure our hotels appear prominently in top local keyword searches, attract business and leisure travelers, and maximize conversion through vanity websites, digital platforms, and automated guest engagement tools. The specialist will manage AI tools and platforms, optimize search performance, support group and transient lead generation, and ensure our hotels are positioned competitively in the digital marketplace.
This role is an in\-person position located at our San Francisco Office.
WHAT WE NEED:
- Bachelors in Business, Hotel Management, Data Science or Computer Science or related field
- Deep expertise in implementing AI, machine learning, or automation tools specifically within the hospitality or tourism sector.
- Strong understanding of hospitality tech stacks, including PMS, POS, and CRM systems.
- Exceptional change\-management skills to ease staff adoption of automated workflows.
- A guest\-first mindset that prioritizes hospitality values over pure technical execution.
- Experience in AI applications, SEO, digital marketing, or hotel technology systems.
- Strong understanding of hotel operations, booking channels, and guest search behavior.
- Familiarity with PMS/CRS systems (SynXis, iHotelier, etc.).Analytical mindset with ability to interpret search data, trends, and performance metrics.
WHAT YOU'LL DO:
- Analyze guest touchpoints to implement AI solutions like smart booking assistants, contactless check\-ins, and automated room controls.
- Work with predictive AI models to optimize room pricing, forecast occupancy, and manage inventory.
- Integrate AI software with Property Management Systems (PMS) if available.
- Train front\-of\-house and back\-of\-house staff to interpret AI insights for better guest service.
- Formulate strict data compliance and security frameworks to protect guest privacy.
- Measure the impact of AI initiatives on operational efficiency and guest satisfaction (NPS).
- AI \& Application Management
- + AI tool administration — Configure, maintain, and optimize AI applications used for guest search, booking funnels, and digital engagement.
+ Automation workflows — Build automated processes to streamline group inquiries, guest requests, and booking pathways
+ Data integration — Connect AI tools with PMS, CRS, CRM, and marketing platforms to ensure accurate, real‑time data flow.
+ AI content generation — Use AI to create SEO‑optimized content, landing pages, and digital assets for hotel visibility.
- Search Optimization \& Digital Visibility
- + Keyword strategy — Identify and optimize top local search terms for business, leisure, group, and event travelers.
+ SEO optimization — Improve ranking across Google, Bing, and travel‑related search platforms using AI‑driven insights.
+ Competitive positioning — Monitor competitor search performance and adjust strategies to maintain top‑tier visibility.
+ Local search dominance — Ensure hotels appear prominently for “hotels near \_\_\_,” “group hotels in \_\_\_,” “business travel \_\_\_,” and other high‑value searches.
- Group \& Guest Search Optimization
- + Group lead generation — Use AI to identify and target group travel opportunities (corporate, SMERF, sports, weddings).
+ Guest search behavior analysis — Track how guests search for hotels and adjust digital strategy accordingly.
+ AI‑powered recommendations — Deploy AI tools that match guest needs with the best hotel offerings.
+ Lead routing — Automate routing of group leads to sales teams for faster response and higher conversion.
WHAT WE OFFER:
- Vacation, Holiday, and Sick pay
- Medical/Dental/Vision (with opt. out option)
- Fitness Reimbursement
- Hotel Room Discount
- Travel Reimbursement
- Life Insurance
- AD\&D
- 401(k) – 4% Match
- Discount programs
- Education Assistance Program
- Long\-Term Disability
- Voluntary Short\-Term Disability
- Voluntary Hospital Insurance, Voluntary Critical Illness Insurance, Voluntary Accident Insurance
- Commuter Benefits
Stanford Hotels Corporation is an Equal Opportunity Employer. All qualified applicants and employees will receive consideration for employment without regard to race; color; sex; gender identity; sexual orientation; religious practices and observances; national origin; pregnancy, childbirth, or related medical conditions; status as a protected veteran or spouse/family member of a protected veteran; or disability. If you need accommodation for any part of the application process because of a medical condition or disability, please send an email to *Rebecca Dawes at rdawes@stanfordhotels.com* or call *415\-266\-9821* to let us know the nature of your request.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Stanford Hotels Corporation, 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 in Demand for This Role
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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Stanford Hotels Corporation AI Hiring
Stanford Hotels Corporation has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Francisco, CA, US.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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