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About This Role
Summary
The Opportunity
Hyatt Hotels Corporation seeks an enthusiastic Sr. AI/ML Engineer to join our Data and Analytics department. In this role, you will be collaborating closely with the broader Machine Learning and AI 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
- Work-life benefits including wellbeing initiatives such as a complimentary Headspace subscription, and a discount at the on-site fitness center
- A global family assistance policy with paid time off following the birth or adoption of a child as well as financial assistance for adoption
- Paid Time Off, Medical, Dental, Vision, 401K with company match
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 Senior AI/ML Engineer builds and operates the shared platforms that AI product engineering teams use to develop and run AI applications, agents, and models. This is a hands-on role focused on reliable, reusable, self-service capabilities that cut duplicated effort and shorten the path from experimentation to production, with internal engineering teams as the customers.
The role delivers scalable AI/ML platforms, including runtimes, orchestration, tool and enterprise system integration, evaluation, and observability, and the real-time serving systems delivered as APIs: supporting products including recommendation engines, information retrieval and search, and other ML and AI backends. Shared services include inference, model deployment pipelines, feature stores, and embedding and vector search services, covering the model and agent lifecycle from versioning through deployment and monitoring. System design is central: defining service boundaries, APIs, and data flows, and making sound tradeoffs across scalability, latency, reliability, security, and cost.
The engineer contributes directly to architecture, Python development, code reviews, and production problem solving across AWS and GCP, partnering with product engineering, data, infrastructure, and security teams. Success is measured by adoption and reliability of the platform and by how consistently product teams ship to production.
Qualifications
Experience Required:
- 7+ years of software engineering experience in cloud-based environments, with a focus on AI engineering, machine learning engineering, platform engineering, or distributed systems, including hands-on ownership of production services.
- Demonstrated experience designing, developing, deploying, and operating shared platforms, reusable services, or developer tools consumed by multiple engineering or data science teams.
- Strong experience with end-to-end system design, including translating technical requirements into architectures, APIs, service interfaces, integration patterns, and production implementations.
- Experience building and operating production AI or machine learning systems, including agent-based applications, model serving, deployment, evaluation, monitoring, or enterprise integrations.
- Experience working independently on complex technical problems, making engineering tradeoffs, and taking accountability for services from initial design through production operations and ongoing improvements.
- Experience collaborating across engineering, data, infrastructure, security, and product teams to deliver scalable and reliable technical solutions.
- Master’s degree in Computer Science, Engineering, Mathematics, Physics, or a related technical discipline, or equivalent practical experience
- Strong system design skills, including distributed systems, service boundaries, APIs, design patterns, data flows, and engineering tradeoffs across reliability, performance, scalability, security, and cost.
- Strong Python development skills, including experience building production services, reusable libraries, APIs, SDKs, and developer tools.
- Experience developing AI agent systems using agent runtimes, orchestration frameworks, tool integrations, memory and context management, retrieval, evaluation, guardrails, and observability.
- Experience with shared AI infrastructure, including real-time and batch inference services, embedding services, vector databases, model deployment pipelines, and feature stores.
- Solid understanding of machine learning fundamentals and lifecycle capabilities, including model training and serving, registration, versioning, validation, evaluation, deployment, monitoring, and explainability.
- Experience with AWS and GCP, including strong hands-on production expertise in at least one. Familiarity with managed AI and machine learning services such as Vertex AI, Gemini, or equivalent technologies.
- Experience with Docker, container orchestration, infrastructure as code, automated testing, and CI/CD practices for building and operating production services.
- Experience with SQL and batch or streaming data processing architectures. Familiarity with PySpark, streaming ingestion, online feature serving, or distributed data processing preferred.
- Experience with service and AI observability, including logging, metrics, distributed tracing, and agent or LLM evaluation tools such as Datadog, Arize, or equivalent technologies.
- Familiarity with agent interoperability and tool integration protocols such as Model Context Protocol (MCP), Agent-to-Agent (A2A), or equivalent frameworks preferred.
- Experience with recommender systems, ranking systems, forecasting, experimentation platforms, or real-time decisioning systems preferred.
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.
The salary range for this position is $133,200 - $173,000
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.
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.
Salary Context
This $133K-$173K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($153K) sits 28% below the category median. Disclosed range: $133K to $173K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Hyatt AI Hiring
Hyatt has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Based in Chicago, IL, US. Compensation range: $173K - $173K.
Location Context
AI roles in Chicago pay a median of $193,440 across 450 tracked positions. That's 9% below 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 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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