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About This Role
Job Title: AI Engineer
Location: New York, NY
Department: Technology/Revenue Strategy
Reports To: Chief Executive Officer
FLSA Status: Exempt
Work Type: Remote Position with occasional travel to the Corporate Office
Employment Type: Full Time
Company: Rebel Hotel Company
About Rebel Hotel Company: Rebel Hotel Company is one of the fastest\-growing third\-party hotel management companies in the United States, recognized for delivering bold results, operational excellence, and distinctive guest experiences. We operate a diverse portfolio of full\-service, lifestyle, and branded hotels across major metropolitan and resort markets. We are building a culture of leadership, innovation, and accountability—and we’re just getting started.
Position Summary: We're a hospitality company building the next generation of intelligent pricing, demand, and guest\-experience systems. Today our commercial teams run rate strategy, RFP negotiations, and account planning on spreadsheets and intuition. We're hiring an AI Engineer to turn that work into software — models and tools that forecast demand, optimize rates, and surface account intelligence so our revenue managers make sharper decisions, faster.
This is a hands\-on engineering role with direct line of sight to revenue. You'll work alongside revenue managers, sales leaders, and data analysts, shipping production systems that touch real pricing and booking decisions across our portfolio.
What You'll Build
- Demand forecasting models that predict occupancy and room\-night demand by property, segment, day\-of\-week, and season — accounting for events, seasonality, and booking pace.
- Dynamic pricing and rate\-optimization engines that recommend BAR and corporate rates, balancing occupancy, ADR, and RevPAR against competitor positioning.
- Account intelligence tooling that ingests internal production data and external signals (M\&A activity, headcount trends, travel\-budget shifts) to flag growing accounts, at\-risk accounts, and uncaptured market opportunity.
- Labor and staffing models that forecast labor demand against projected occupancy and arrivals — optimizing schedules, hours, and cost across housekeeping, front desk, F\&B, and other departments while protecting service levels.
- RFP and negotiation support tools that help the team price contracts, model rate scenarios, and prioritize target accounts during RFP season.
- LLM\-powered workflows — summarizing market intelligence, drafting account strategy notes, and answering natural\-language questions over revenue data.
- A testing laboratory for beta technologies — stand up and run a controlled environment where new AI tools and models can be piloted, stress\-tested, and validated against real operational data before broader rollout, including the experimentation framework, sandboxed data, and feedback loops with property and commercial teams
What You'll Do
- Design, train, evaluate, and deploy machine learning models on real booking, rate, and market data.
- Build data pipelines that bring together PMS, CRS, booking\-channel, and third\-party market data into clean, reliable feature sets.
- Stand up the infrastructure to serve models in production — APIs, monitoring, retraining, and guardrails.
- Operate the beta testing lab — design pilots, recruit internal users, measure results, and decide what graduates to production versus what gets killed.
- Partner closely with revenue and commercial teams to translate domain knowledge into product, and to make sure outputs are trustworthy and actionable.
- Define and track the metrics that matter — forecast accuracy, recommendation adoption, labor cost and productivity, and downstream revenue impact.
What We’re Looking For
Required
- 3\+ years building and shipping ML systems in production (not just notebooks).
- Strong Python and the modern ML/data stack (e.g. pandas, scikit\-learn, PyTorch or TensorFlow, SQL).
- Solid grounding in forecasting, optimization, or recommendation/pricing problems.
- Experience taking models from prototype to deployed service, including monitoring and iteration.
- Ability to communicate clearly with non\-technical stakeholders and translate business problems into technical ones.
Nice to have
- Experience in hospitality, travel, airlines, retail, or another revenue\-management\-driven industry.
- Familiarity with dynamic pricing, demand modeling, or yield/revenue management.
- Experience integrating LLMs into applications (RAG, structured extraction, agentic workflows).
- Cloud and MLOps experience (AWS/GCP/Azure, containerization, CI/CD for ML).
- Comfort with experimentation and causal measurement (A/B testing, uplift modeling).
What We Offer:
- Competitive base salary and performance\-based incentive plan
- Medical, dental, and vision insurance
- 401(k) plan with company match
- Paid time off and holidays
- Career advancement opportunities within a rapidly growing company
- A chance to be part of the Rebel movement redefining hospitality leadership
Why This Role
You'll own meaningful problems end\-to\-end, see your work move real revenue, and help build a data and AI capability from an early stage. If you want your models to ship and matter rather than sit in a backlog, this is that role.
Salary Range: $190,000 \- $200,000 annually
At Rebel Hotel Company, we don’t manage hotels the old way—we challenge the status quo. If you’re ready to lead with vision, act with ownership, and make your mark in the hospitality world, we want to meet you.
Salary Context
This $190K-$200K range is above the median 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 Rebel Hotel 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. Mid-level AI roles across all categories have a median of $165,000. This role's midpoint ($195K) sits 8% above the category median. Disclosed range: $190K to $200K.
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.
Rebel Hotel Company AI Hiring
Rebel Hotel Company has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% 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 3,823 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
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