ml engineer for revenue management

Cloudbeds provides an intelligently designed hospitality platform for properties across 150 countries, integrating with hundreds of partners to support hotel operations and commercial strategy. Its machine learning team develops AI-driven insights and predictive algorithms that help lodging businesses improve operational efficiency, revenue management, and pricing decisions.

• Build and implement features that help lodging customers make data-driven pricing decisions;
• Develop heuristic and machine learning solutions to optimize hotel revenue strategies;
• Work with product and engineering teams to identify improvement opportunities and develop innovative solutions;
• Ensure the reliability, scalability, and quality of ML systems from development through production;
• Establish robust ML practices and testing processes across the ML lifecycle;
• Structure data pipelines and 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, including Sagemaker;
• Expert-level knowledge of CI/CD, orchestration such as Apache Airflow and Flink, and model monitoring and drift detection at scale;
• Strong Python, distributed systems, and backend development skills;
• 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 with MLFlow, Sagemaker, or similar platforms;
• Knowledge of 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, a Master’s or PhD in Computer Science, Mathematics, or a related field.

• Remote-first and remote-always work environment;
• 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 manager training, upskilling, and knowledge transfer.