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As a Sales Manager, you will identify and acquire new business opportunities, build relationships, and demonstrate Cloudbeds' SaaS solutions to clients in the hospitality industry.

Lead data migrations and clean, map, and validate client CSV/Excel files into Cloudbeds. Troubleshoot formatting issues, run post-migration audits, coach clients on building and automating reports, and advise on KPIs to ensure accurate financial and operational insights.

Lead high-touch post-launch support for hotels as they go live on Cloudbeds. Manage urgent ticket queues and hotlines, troubleshoot integrations and live data flows, use AI and automation for efficiency, conduct proactive follow-ups, document account health in CRM, and transition clients to long-term account teams while sharing product feedback cross-functionally.

As a Sales Manager at Cloudbeds, you'll expand business presence by acquiring new clients, identifying market trends, and showcasing SaaS solutions in hospitality.

The Sales Manager is responsible for acquiring new business opportunities, building relationships with prospects, and showcasing SaaS solutions in the hospitality sector through outreach and product demonstrations.

Design, deploy, and maintain production-grade ML systems for hotel revenue management. Build data pipelines, implement and validate models, establish MLOps practices (CI/CD, orchestration, monitoring), define SLIs/SLOs, and collaborate with product and engineering to drive scalable, reliable ML-driven pricing features.

Owner of end-to-end ML systems for revenue management: design, deploy, and maintain production-grade models and data pipelines. Define ML testing, SLIs/SLOs, monitoring and drift detection. Collaborate with product and engineering, mentor engineers, and ensure scalable, reliable ML-powered pricing and revenue features for hotel customers.

Design, build, and maintain production-grade ML systems for revenue management. Own end-to-end ML lifecycle: data pipelines, model development, deployment, monitoring, and testing. Collaborate with product and engineering, define SLIs/SLOs, mentor engineers, and drive technical strategy to deliver scalable, reliable pricing and revenue-optimization features.

Design, deploy, and maintain production-grade distributed ML systems for revenue management. Build data pipelines, implement and validate models, establish MLOps practices (CI/CD, orchestration, monitoring), define SLIs/SLOs, and collaborate with product and engineering to drive model reliability and business impact.

Design, build, and maintain production-grade ML systems for revenue management. Own end-to-end ML lifecycle: data pipelines, model deployment, monitoring, testing, and scaling. Collaborate with product and engineering to define SLIs/SLOs, optimize pricing strategies, and mentor cross-functional teams to ensure reliable, high-quality ML-driven features.