THE COMPANY
We are an ambitious, well-funded, high-growth global technology company transforming the hotel industry. At Duetto, we are passionate about creating innovative analytical solutions to help hoteliers thrive. Although we work hard, the work atmosphere is casual, flexible, collaborative, and most of all, fun.
Duetto offers an open and collaborative work environment and believes that by cultivating a team with diverse backgrounds, perspectives, and experiences, it will continue to lead the industry with its cutting-edge platform-based hospitality technology.
Introduction
We are seeking a Machine Learning Engineer to help build and scale our machine learning infrastructure and workflows. At Duetto, you’ll take on the unique challenge of supporting the development, training, deployment, and monitoring of thousands of machine learning models, one for each hotel customer.
You’ll work closely with data scientists, DevOps, and platform engineers to deliver robust, reusable tooling for the entire ML lifecycle—including training pipelines, inference APIs, feature workflows, and monitoring hooks—within our AWS-native environment. Your work will help us ensure that ML models are delivered quickly, reliably, and cost-effectively into production.
This is an opportunity to build ML systems at scale, contribute to the design of modern ML infrastructure on top of AWS and Kubernetes, and shape the future of machine learning at Duetto.
Key Responsibilities:
Qualifications:
About Duetto:
Duetto delivers a suite of SaaS cloud-native applications for hospitality businesses to optimize every booking opportunity for greater revenue impact. The unique combination of hospitality experience and technology leadership drives Duetto to look for innovative solutions to industry challenges. The software as a service platform allows hotels, casinos, and resorts to leverage real-time dynamic data sources and actionable insights into pricing and demand across the enterprise. For more information, please visit https://www.duettocloud.com/.
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