Machine Learning Teaching Remote Jobs in Chicago, IL

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.
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
• 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 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