Senior MLOps Engineer in Bristol

About Cloudbeds

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.

Location: Remote with expected travel into Paddington 2 days per week.

How You'll Make an Impact

As a Machine Learning Ops 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’ll 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’ll be instrumental in establishing robust MLOps practices and rigorous testing processes across the entire ML lifecycle. From structuring data pipelines to implementing and validating ML models, you’ll own the end‑to‑end development of our revenue management application—ensuring hotels have the reliable, accurate insights they need to maximize their success.

What You Bring to the Team

• Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies, with a strong emphasis on production readiness and system reliability.
• Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
• Design, build, and maintain highly reliable and well‑tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
• Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
• Implement comprehensive software quality and testing processes for ML systems, including unit, integration, and end‑to‑end testing for both code and data/model performance.
• Design, train, and rigorously test machine‑learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
• Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
• Collaborate cross‑functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
• Conduct structured A/B testing and experimentation to validate model effectiveness and continuously improve performance, documenting results and sharing technical insights.

What Sets You Up for Success

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
• 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
• Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
• Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
• Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
• 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.

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, Data Science, or a related field.
• Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

Benefits

• Remote First, Remote Always
• PTO in accordance with local labor requirements
• Monthly Wellness Fridays - enjoy an extra long weekend every month
• Full 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.

EEO Statement

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. Cloudbeds is committed to the full inclusion of all qualified individuals. As part of this commitment, Cloudbeds will ensure that persons with disabilities are provided reasonable accommodations in the hiring process. We encourage deaf, hard of hearing, deaf‑blind, and deaf‑disabled individuals to apply. If reasonable accommodation is needed to participate in the job application or interview process or to perform essential job functions, please contact our HR team by phone at (858) 201-7832 or via email at accommodations@cloudbeds.com. Cloudbeds will provide an American Sign Language (ASL) interpreter where needed as a reasonable accommodation for the hiring processes.