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About This Role
Summary
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The Opportunity
Hyatt Hotels Corporation seeks an enthusiastic Senior ML Engineer to join our Data Science and Machine Learning department. In this role, you will be collaborating closely with the broader Data and Analytics team, where you’ll be instrumental in continuing to make Hyatt a leading hospitality company. You will be part of a team that is passionate about our purpose, committed to nurturing curiosity and new skills, and building connections across the organization with colleagues, customers, and guests.
Who We Are
At Hyatt, we believe in the power of belonging and creating a culture of care, where our colleagues become family. Since 1957, our colleagues and our guests have been at the heart of our business and helped Hyatt become one of the best and fastest\-growing hospitality brands in the world. Our transformative growth and the addition of new hotels, brands, and business lines can open the door for exciting career and growth opportunities for our colleagues.
As we continue to grow, we never lose sight of what’s most important: People. We turn trips into journeys, encounters into experiences, and jobs into careers.
Why Now?
This is an exciting time to be at Hyatt. We are growing rapidly and are looking for passionate changemakers to be a part of our journey. The hospitality industry is resilient and continues to offer dynamic opportunities for upward mobility, and Hyatt is no exception.
How We Care for Our People
What sets us apart is our purpose—to care for people so they can be their best. Every business decision is made through the lens of our purpose, and it informs how we have and will continue to support each other as members of the Hyatt family. Our care for our colleagues is the key to our success. We’re proud to have earned a place on Fortune’s prestigious *100 Best Companies to Work For®* list since 2013\. This recognition is a testament to the tremendous way our Hyatt family continues to come together to care for one another, our commitment to a culture of inclusivity, empathy, and respect, and making sure everyone feels like they belong.
We’re proud to offer exceptional corporate benefits which include:
- Annual allotment of free hotel stays at Hyatt hotels globally
- Flexible work schedule
- A global family assistance policy with paid time off following the birth or adoption of a child as well as financial assistance for adoption
- Extended Health Benefits for you and your dependents and paid medical days
- Employer RRSP Matching Contributions
- Fitness and Wellness Allowance
- Cell Phone Allowance
Who You Are
As our ideal candidate, you understand the power and purpose of our culture of care, and embody our core values of Empathy, Inclusion, Integrity, Experimentation, Respect, and Wellbeing. You enjoy working with others, are results\-driven, and are looking for a variety of opportunities to develop personally and professionally.
The Role
The Machine Learning Engineer partners with data science, data engineering, and platform teams to design, build, and operate scalable AI services. This role is responsible for translating machine learning models into reliable, production\-grade systems through strong infrastructure design, MLOps automation, and performance optimization. The position also contributes to cross\-functional initiatives that advance the organization’s AI platform capabilities.
Responsibilities
- Design and implement end\-to\-end ML systems, including data ingestion, feature processing, model training, and model serving
- Architect and deploy scalable AI services supporting real\-time and batch inference use cases
- Build and maintain ML infrastructure across cloud environments (e.g., EC2, EKS, SageMaker, specialized inference hardware)
- Develop and evolve MLOps platforms, including training pipelines, deployment workflows, feature stores, and model observability
- Implement CI/CD and infrastructure\-as\-code patterns to automate model lifecycle management
- Optimize model training and inference performance for cost, latency, and hardware efficiency
- Monitor production ML systems for accuracy, reliability, and operational health
- Partner cross\-functionally with data engineering, architecture, governance, and security teams to ensure compliant and scalable solutions
- Mentor team members on ML engineering, system design, and operational best practices
- Contribute to special initiatives that advance AI platform maturity and engineering standards
Qualifications
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Experience Required:
- Master’s degree in Computer Science, Software Engineering, Machine Learning, or a related field
- 5\+ years of experience building and operating machine learning solutions in cloud environments, with focus on AI services and MLOps foundations
- Demonstrated hands\-on experience delivering end\-to\-end ML systems, spanning model development, deployment, and production infrastructure
- Proficiency with modern ML engineering tooling, including cloud platforms, data pipelines, and CI/CD workflows
Experience Preferred
- Experience designing and scaling real\-time and batch inference systems in production
- Hands\-on experience with deep learning frameworks and model optimization for performance and cost
- Experience building or contributing to shared MLOps platforms, feature stores, or ML observability solutions
- Familiarity with cloud security, governance, and compliance standards
The position responsibilities outlined above are in no way to be construed as all\-encompassing. Other duties, responsibilities, and qualifications may be required and/or assigned as necessary.
We welcome you:
Research shows that individuals tend to apply to jobs only if they meet all the listed job qualifications. Unsure if you check every box, but feeling inspired to enhance your career? Apply. We’d love to consider your unique experiences and how you could make Hyatt even better.
*We value our relationships with recruitment partners and require that agencies contact us first before submitting any candidates. Hyatt will not be responsible for any fees and obligations associated with unsolicited submissions unless a formal agreement is in place.*
The salary range for this position is CAD90,000 \- CAD110,000\. This position is also eligible to earn an annual bonus.
*The final pay rate/salary offered to the successful candidate will depend on experience, skill level and other qualifications for the role, as well as the location of the performance of work. Pay for the successful candidate will meet local requirements, including the local minimum wage rate.*
Candidates must be legally authorized to work in Canada at the time of application and throughout their employment. Proof of eligibility to work in Canada will be required. Unless otherwise indicated in the job posting, Hyatt does not sponsor employment visas or work permits for this position. Applications from candidates who do not meet these requirements will not be considered.
Hyatt is committed to providing an inclusive and accessible recruitment experience. In accordance with applicable human rights and accessibility legislation across Canada, accommodation is available throughout the recruitment and selection process. If you require accommodation at any stage, please notify Human Resources, and we will work with you to meet your accessibility needs.
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 Hyatt, 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. Senior-level AI roles across all categories have a median of $227,400.
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.
Hyatt AI Hiring
Hyatt has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Chicago, IL, US.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
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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