At a Glance
- Tasks: Build AI-powered solutions to optimise revenue strategies for hotels worldwide.
- Company: Join Cloudbeds, a leader in transforming hospitality with innovative technology.
- Benefits: Enjoy remote work, competitive pay, wellness Fridays, and professional development opportunities.
- Why this job: Make a real impact in the hospitality industry while working with cutting-edge machine learning technologies.
- Qualifications: 3+ years in data engineering or machine learning, strong Python and SQL skills required.
- Other info: Be part of a diverse, global team passionate about innovation and travel.
The predicted salary is between 36000 - 60000 £ per year.
At Cloudbeds, we are transforming hospitality. Our platform powers properties across 150 countries, processing billions in bookings annually. We help hoteliers transform operations and uplevel their commercial strategy through a unified platform that integrates with hundreds of partners. Imagine working alongside global innovators to build AI-powered solutions that solve hoteliers' biggest challenges.
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 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.
What You Bring to the Team:
- Develop and implement end-to-end machine learning features that enable customers to optimize their revenue strategies.
- 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.
- Expertise in using Apache Airflow (or similar orchestration tools) to define, schedule, and monitor complex data and ML workflows.
- Implement comprehensive software quality and testing processes for ML systems.
- Design, train, and rigorously test machine learning models to improve pricing optimization.
- Implement model performance monitoring to ensure deployed models maintain accuracy and relevance over time.
- Collaborate cross-functionally with product, engineering, and data science teams.
- Conduct structured A/B testing and experimentation to validate model effectiveness.
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.
- Proven expertise in designing and implementing ML testing strategies.
- Expertise in deploying ML models at scale on AWS.
- Strong Python programming skills and adherence to software engineering best practices.
- Expert-level SQL skills and experience working with large datasets.
- Strong problem-solving skills with the ability to apply creative, data-driven solutions.
- Excellent communication and collaboration skills.
Bonus Skills to Stand Out (Optional):
- Experience with CI/CD tooling specifically for ML pipelines.
- Experience with data quality monitoring tools and frameworks.
- Master’s or PhD in Computer Science, Data Science, or a related field.
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. If you’re ready to work alongside some of the brightest minds in tech who are obsessed with using AI to transform a trillion-dollar industry, this is your chance to be part of something extraordinary.
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, sexual orientation, gender identity, age, status as a protected veteran, status as an individual with a disability, or other applicable legally protected characteristics.
MLOps Engineer employer: Third-Party Job Posts
Contact Detail:
Third-Party Job Posts Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land MLOps Engineer
✨Network Like a Pro
Get out there and connect with people in the industry! Attend meetups, webinars, or even just chat with folks on LinkedIn. Building relationships can open doors that job applications alone can't.
✨Show Off Your Skills
Don’t just tell them what you can do; show them! Create a portfolio of your projects or contributions to open-source. This gives potential employers a taste of your capabilities and makes you stand out.
✨Ace the Interview
Prepare for interviews by practicing common questions and showcasing your problem-solving skills. Use the STAR method (Situation, Task, Action, Result) to structure your answers and highlight your achievements.
✨Apply Through Our Website
When you find a role that excites you, apply directly through our website! It shows you're genuinely interested and helps us keep track of your application better. Plus, it’s super easy!
We think you need these skills to ace MLOps Engineer
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 our mission at Cloudbeds!
Showcase Your Projects: Include specific examples of projects you've worked on that demonstrate your expertise in MLOps and model deployment. Whether it's a personal project or something from your previous job, 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. We appreciate straightforward communication, especially when it comes to your achievements and experiences.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way to ensure your application gets into the right hands. Plus, it shows us you're keen on joining our remote team at Cloudbeds!
How to prepare for a job interview at Third-Party Job Posts
✨Know Your MLOps Inside Out
Make sure you brush up on your MLOps knowledge before the interview. Understand the key concepts like CI/CD for model training and deployment, as well as how to maintain system reliability. Be ready to discuss your experience with tools like Apache Airflow and how you've implemented robust MLOps practices in your previous roles.
✨Showcase Your Problem-Solving Skills
Prepare to share specific examples of how you've tackled complex challenges in machine learning. Think about times when you applied creative, data-driven solutions to improve system performance or optimise revenue strategies. This will demonstrate your ability to think critically and innovate, which is crucial for the role.
✨Collaborate Like a Pro
Since this role involves working closely with product and engineering teams, be ready to discuss your collaboration experiences. Highlight instances where you successfully worked cross-functionally to define SLIs/SLOs or improve system usability. This shows that you can communicate effectively and contribute to a team environment.
✨Prepare for Technical Questions
Expect technical questions related to machine learning models, data pipelines, and testing strategies. Brush up on your Python programming skills and SQL expertise, as these are essential for the role. Practising coding problems or discussing your past projects can help you feel more confident during the technical portion of the interview.