ml engineer for hospitality revenue management

Cloudbeds provides an intelligent hospitality platform used by properties across 150 countries. The platform unifies hotel operations and commercial strategy through integrations with hundreds of partners, including AI-powered solutions for revenue management and pricing.

• Build and implement features that enable lodging customers to make data-driven pricing decisions;
• Develop solutions using heuristic data and advanced machine learning techniques to optimize revenue strategies;
• Identify improvement opportunities with product and engineering teams;
• Develop innovative solutions that drive revenue growth for hotels;
• Ensure the reliability, scalability, and quality of ML systems from development through production;
• Establish robust ML practices and rigorous testing processes across the ML lifecycle;
• Structure data pipelines;
• Implement and validate ML models;
• Own the end-to-end development of the revenue management application;
• Define SLIs/SLOs and manage large-scale technical roadmaps;
• Influence cross-functional teams, mentor junior talent, and drive consensus on complex technical decisions.

• 5+ Years of experience in a machine learning role, with demonstrated success in ML Engineering and deploying models to production;
• Proven experience designing, deploying, and maintaining production-grade distributed ML systems using Sagemaker;
• Expert knowledge of CI/CD, orchestration such as Apache Airflow and Flink, and model monitoring or drift detection at scale;
• Strong Python, distributed systems, and backend development experience;
• Experience designing and implementing ML testing strategies, including data validation, model correctness, and performance testing;
• Strong understanding of machine learning principles, experimental design, statistical distributions and tests, and machine learning algorithms;
• Experience deploying ML models at scale on AWS using MLFlow, Sagemaker, or similar platforms;
• Strong Python programming skills and adherence to software engineering best practices, including clean code, version control, code reviews, Docker, Terform, and Kubernetes;
• Expert-level SQL skills and experience working with large datasets for analysis and modeling;
• Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges;
• Excellent communication and collaboration skills, including cross-functional work with product and engineering teams;
• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field;
• Nice to have: CI/CD tooling such as GitHub Actions and Jenkins for ML pipelines and Airflow DAG deployment, data quality monitoring tools and frameworks, Master's or PhD in Computer Science, Mathematics, or a related field.

• Remote work across Europe;
• PTO in accordance with local labor requirements;
• Monthly Wellness Fridays;
• Fully paid parental leave;
• Home office stipend based on country of residency;
• Professional development courses in Cloudbeds University;
• Access to professional development, including manager training, upskilling, and knowledge transfer.