Staff ML Engineer

Cloudbeds; Dublin; On-site; EUR 120,000 - 155,000; Full time

Job summary

Cloudbeds is seeking a Staff Machine Learning Engineer to architect and deliver production‑grade ML systems for revenue management and pricing strategies. You will own end‑to‑end ML development, from data pipelines to validated models, collaborating with product and engineering to scale impact.

Ideal candidates have 5+ years in ML, strong MLOps, and a track record deploying models to production with rigorous testing and monitoring.

Qualifications

• 5+ years in ML/ML engineering with production deployments.
• Experience designing ML testing strategies (data validation, model correctness).
• Strong understanding of ML principles and performance considerations.

Responsibilities

• Lead end‑to‑end ML lifecycle from data pipelines to production models.
• Collaborate with product/engineering to propose revenue‑driving ML features.
• Establish robust ML practices, monitoring, and testing across models and deployments.

Skills

Architectural Expertise

MLOps

Python & Back‑end

Technical Strategy

Leadership

Domain Knowledge

Tools

Sagemaker

Job description

What Makes Cloudbeds Unique

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. Imagine working alongside global innovators to build AI-powered solutions that solve hoteliers' biggest challenges. Since our founding in 2012, we've become the World's Best Hotel PMS Solutions Provider and landed on Deloitte's Technology Fast 500 again in 2024, but we're just getting started.

Location - Remote (Europe)

How You'll Make an Impact:

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’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 ML 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.

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.

People who are passionate about continuous learning, unafraid to challenge conventions, and excited by the intersection of hospitality and deep technical prowess will find their home among our forward‑thinking team.

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