At a Glance
- Tasks: Build AI-powered solutions to transform hospitality and optimise revenue strategies.
- Company: Join Cloudbeds, a leader in hotel management software with a remote-first culture.
- Benefits: Enjoy remote work, professional development, and wellness Fridays every month.
- Other info: Be part of a diverse team passionate about innovation and travel.
- Why this job: Make a real impact in the hospitality industry using cutting-edge machine learning technology.
- Qualifications: 5+ years in ML engineering, strong Python skills, and experience with AWS.
The predicted salary is between 60000 - 80000 £ per year.
At Cloudbeds, we're not just building software, we're transforming hospitality. Our intelligently designed platform powers properties across 150 countries, processing billions in bookings annually. From independent properties to hotel groups, we help hoteliers transform operations and uplevel their commercial strategy through a unified platform that integrates with hundreds of partners. And we do it with a completely remote team.
As a Staff Machine Learning Engineer, you will play a key role in building and implementing features that empower lodging customers to make data-driven pricing decisions. Some of these features will use simple heuristic data, while others will leverage advanced machine learning techniques to optimize revenue strategies.
You will work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for the hotels that rely on our platform. Your impact will be focused on ensuring the reliability, scalability, and high quality of our ML systems from development to production. You will be instrumental in establishing robust ML practices and rigorous testing processes across the entire ML lifecycle.
Our machine learning team is energized by the unique challenge of revolutionizing guest experiences through AI-driven insights, transforming traditional hospitality with cutting-edge predictive algorithms. We thrive on collaborative innovation, where data scientists, engineers, and product experts seamlessly blend their expertise to prototype bold ideas and directly impact operational efficiency.
What You Bring to the Team:
- Architectural Expertise: Proven track record in designing, deploying, and maintaining production-grade, distributed ML systems (Sagemaker).
- Deep MLOps Proficiency: Expert-level knowledge of CI/CD, orchestration (e.g., Apache Airflow, Flink), and model monitoring/drift detection at scale.
- Software Engineering Rigor: Strong background in Python, distributed systems, and backend development, with a firm grasp of software engineering best practices.
- Technical Strategy: Experience defining SLIs/SLOs and managing large-scale technical roadmaps.
- Leadership: Demonstrated ability to influence cross-functional teams, mentor junior talent, and drive consensus on complex technical decisions.
- Domain Knowledge: Ability to apply statistical and ML methods to optimize revenue management and pricing strategies.
What Sets You Up for Success:
- 5+ years of experience in a machine learning role, with demonstrated success in ML Engineering and deploying models to production.
- Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
- Great understanding of machine learning principles (experimental design, statistical distributions and test, machine learning algorithms).
- Expertise in deploying ML models at scale on AWS, with experience using MLFlow, Sagemaker or similar platforms.
- Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews, using Docker, Terraform, 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, with experience working cross-functionally with product and engineering teams.
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
Bonus Skills to Stand Out (Optional):
- Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
- Experience with data quality monitoring tools and frameworks.
- Master's or PhD in Computer Science, Mathematics, or a related field.
Cloudbeds is proud to be an Equal Opportunity Employer that celebrates the diversity in our global team! We do not discriminate based upon race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
