Description

Summary

At Hyatt, we're working to Advance Care through data-driven decisions and automation. This mission serves as the foundation for every decision as we create the future of travel. We can't do that without the best talent - talent that is innovative, curious, and driven to create exceptional experiences for our guests, customers, owners and colleagues.
Hyatt seeks an extraordinary Machine Learning Engineer to help build the algorithmic assets and features that Hyatt guests, members, customers and internal users leverage to transform the guest experience and drive efficiencies across the operations of our business.

In this role you will design and implement algorithmic product architectures across Personalization, Generative AI, Forecasting, and Decision Science domains, as well as foundational MLOps frameworks to bring our machine learning models to life across the full lifecycle of the product including data ingestion, ML processing, and results delivery/activation. This role combines deep technical modeling expertise with infrastructure engineering skills to design, build, and operate end-to-end ML/AI systems at scale. You'll work across the full ML lifecycle - from distributed training and model optimization to production deployment and monitoring.
This role will work cross-functionally with various data science teams, data engineering teams, and data architecture teams. The ideal candidate can serve as both solutions architect as well as hands-on implementation engineer and guide the team towards best-in-class MLOps platform and algorithmic product implementations.
You will be a part of a ground-floor, hands-on, highly visible team which is positioned for growth and is highly collaborative and passionate about machine learning and AI. Applying the latest techniques and approaches across the domains of data science, machine learning, and AI isn't just a nice to have, it's a must.

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 to 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 for the last ten years. 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 and location
• 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

Our Commitment to Diversity, Equity, and Inclusion
Our success is underpinned by our diverse, equitable and inclusive culture and we are committed to diversity across the board-from who we hire and develop, organizations we support, and who we buy from and work with.
Being part of Hyatt means always having space to be you. Our global teams are a mosaic of cultures, ethnicities, genders, ages, abilities and identities. We constantly strive to reflect the world we care for with teams that achieve and grow together. To learn more about our commitments to DE&I, please visit the Why Hyatt section of the Hyatt career page.

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.

Qualifications

Infrastructure Design & AI-Services Architecture
• Partner with data scientists to design AI-services and architectures that activate ML models and maximize their impact, such as real-time streaming use-cases and offline batch optimizations
• Lead the design and implementation of ML infrastructure solutions, including data ingestion pipelines, feature processing, model training, and serving environments
• Build and maintain scalable inference systems for real-time and batch predictions
• Deploy models across various compute environments (EC2, EKS, SageMaker, specialized inference chips)
MLOps Platform & Pipeline Automation
• Implement, evolve, and maintain our MLOps platform, technology, and processes; including Feature Store, ML Observability, ML Governance, Training and Deployment pipelines
• Create and maintain automated workflows for model training, evaluation, and deployment using infrastructure-as-code patterns
• Build MLOps platforms and tooling that abstract complex engineering tasks for data science teams
• Implement CI/CD pipelines for both model artifacts and infrastructure components
Model Development & Optimization
• Design, implement, and optimize machine learning models including deep learning architectures, LLMs, and specialized models (e.g., BERT-based classifiers) across Personalization, Generative AI, Forecasting, and Decision Science domains
• Implement distributed training workflows using PyTorch and other frameworks
• Fine-tune large language models and optimize inference performance using model compilation and optimization tools (Neuron compiler for AWS Inferentia, ONNX, vLLM)
• Optimize models for specific hardware targets (GPU, TPU, AWS Inferentia/Trainium)
Performance & Operational Excellence
• Enhance and maintain existing AI-services as needed to maximize impact of the algorithmic product
• Monitor ML systems for performance, accuracy, latency, and cost optimization
• Conduct performance profiling and optimization of training and inference workloads
• Implement observability and monitoring solutions across the ML stack
Cross-functional Partnership & Technical Leadership
• Partner with data engineering team to ensure data science data needs are being delivered in the appropriate format/cadence required for maximum impact
• Partner with data architecture, data governance, and security team to ensure solutions meet required standards
• Mentor team members on both modeling techniques and infrastructure best practices
• Stay up to date with latest AI and MLOps design patterns as well as AWS services with respect to Machine

Learning Engineering Qualifications

Experience Required:
• Master's degree in Computer Science, Software Engineering, Machine Learning, or related fields required
• 5+ years of implementing AI solutions in a cloud environment with a focus on AI-services and MLOps foundations. Hospitality experience not required
• 3+ years of hands-on experience with both ML model development and production infrastructure
Technical Competencies:
• Cloud & Infrastructure: Expertise in AWS cloud services (EC2, EKS, S3, SageMaker, Inferentia/Trainium), Terraform/CloudFormation, Docker, Kubernetes
• Data & Processing: Expertise in Python, SQL, PySpark, Apache Spark, Airflow, Kinesis, feature stores, model serving frameworks
• Development & Operations: Experience with streaming and batch data architectures at scale, DevOps and CI/CD concepts (GitHub Actions, CodePipeline), monitoring (CloudWatch, Prometheus, MLflow)
• Machine Learning & Deep Learning: PyTorch, TensorFlow, distributed training, LLM fine-tuning, transformer architectures, model optimization, ONNX, vLLM, hardware-specific optimizations

Additional Requirements:
• Experience operating in an Agile Methodology environment
• Experience building end-to-end ML systems from research to production
• Excellent communication and teamwork skills
• Position will not require customer-facing interactions
Desired Qualifications:
• Previous work on recommendation systems, NLP applications, or real-time inference systems
• Experience with MLOps platform development and feature store implementations
• Familiarity with security and compliance standards in cloud environments

Note: This role requires expertise across both ML engineering and infrastructure domains, combining cutting-edge modeling work with production infrastructure engineering. You'll have ownership over the complete ML lifecycle and the chance to build systems that directly impact business outcomes.
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 women, people of color and other historically excluded groups, tend to apply to jobs, only if they meet all of 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 $130,000 to $170,000. This position is also eligible to earn incentive awards and 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.