At a Glance
- Tasks: Build and implement machine learning features to optimise pricing for lodging customers.
- Company: Join Cloudbeds, a leader in transforming hospitality with innovative technology.
- Benefits: Enjoy remote work, wellness Fridays, and professional development opportunities.
- Other info: Be part of a diverse team redefining what's possible in hospitality.
- Why this job: Make a real impact in the hospitality industry using cutting-edge machine learning.
- Qualifications: 3+ years in data engineering or machine learning, strong Python and SQL skills.
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. 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.
- You’ll work closely with product and engineering teams to identify opportunities for improvement, develop innovative solutions, and drive revenue growth for hotels that rely on our platform.
- Your impact will focus on ensuring the reliability, scalability, and high quality of our ML systems from development to production.
What you bring to the team:
- Develop and implement end‑to‑end machine learning features that enable customers to optimize 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).
What to expect – your journey with us:
Behind Cloudbeds’ revolutionary technology is a team redefining what’s possible in hospitality. We’re 650+ employees across 40+ countries, bringing together elite engineers, AI architects, world‑class designers, and hospitality veterans to solve challenges others haven’t dared to tackle.
Discover our 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.
Everyone is welcome – a culture of inclusion:
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.
To all Staffing and Recruiting Agencies: Our Careers Site is only for individuals seeking a job at Cloudbeds. Staffing, recruiting agencies, and individuals being represented by an agency are not authorized to use this site or to submit applications, and any such submissions will be considered unsolicited. Cloudbeds does not accept unsolicited resumes or applications from agencies. Please do not forward resumes to our jobs alias, Cloudbeds employees, or any other company location. Cloudbeds is not responsible for any fees related to unsolicited resumes/applications.
MLOps Engineer in Salford employer: Cloudbeds
At Cloudbeds, we pride ourselves on being a remote-first employer that champions a culture of inclusion and professional growth. With generous benefits like monthly wellness Fridays, full paid parental leave, and access to continuous learning through Cloudbeds University, we empower our employees to thrive both personally and professionally. Join a diverse team of over 650 talented individuals dedicated to transforming the hospitality industry while enjoying the flexibility of remote work and the opportunity to collaborate in our Paddington office twice a week.
StudySmarter Expert Advice🤫
We think this is how you could land MLOps Engineer in Salford
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, especially those at Cloudbeds. A friendly chat can open doors and give you insights that a job description just can't.
✨Tip Number 2
Show off your skills! If you've got a portfolio or GitHub with projects related to MLOps, make sure to highlight them. Real-world examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for the interview by brushing up on your technical knowledge. Be ready to discuss your experience with CI/CD, model deployment, and data pipelines. Confidence in your expertise will shine through!
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, it shows you're genuinely interested in joining the Cloudbeds team. Let's get you on board!
We think you need these skills to ace MLOps Engineer in Salford
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the MLOps Engineer role. Highlight your experience with machine learning, data pipelines, and any relevant tools like Apache Airflow. We want to see how your skills align with what we’re looking for!
Showcase Your Projects:Include specific examples of projects you've worked on that demonstrate your expertise in MLOps. Whether it’s deploying models or implementing CI/CD practices, we love seeing real-world applications of your skills!
Be Clear and Concise:When writing your application, keep it clear and to the point. Use bullet points where possible to make it easy for us to read through your qualifications and experiences. We appreciate a well-structured application!
Apply Through Our Website:Don’t forget to submit your application through our website! It’s the best way for us to receive your details and ensures you’re considered for the role. We can’t wait to hear from you!
How to prepare for a job interview at Cloudbeds
✨Know Your MLOps Inside Out
Make sure you’re well-versed in MLOps practices, especially CI/CD for model training and deployment. Brush up on your experience with tools like Apache Airflow or similar orchestration tools, as these will likely come up during the interview.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific examples where you've applied creative, data-driven solutions to complex challenges. Think of scenarios where you improved system reliability or optimised revenue strategies, as this aligns perfectly with what Cloudbeds is looking for.
✨Demonstrate Collaboration Experience
Since the role involves working closely with product and engineering teams, be ready to share experiences that highlight your communication and collaboration skills. Discuss how you’ve successfully worked cross-functionally to achieve common goals.
✨Prepare for Technical Questions
Expect technical questions related to machine learning models, data pipelines, and performance monitoring. Brush up on your Python programming skills and SQL expertise, and be prepared to explain your approach to testing strategies and model validation.