MLOps Engineer

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.

Our Machine Learning Team:

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:

1. 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.
2. Establish and maintain robust MLOps practices including CI/CD for model training, testing, deployment, and monitoring.
3. Design, build, and maintain highly reliable and well-tested data and ML pipelines to extract, transform, and structure large datasets for ML applications.
4. Expertise in using Apache Airflow (or similar orchestration tools like Prefect/Dagster) to define, schedule, and monitor complex data and ML workflows (DAGs).
5. 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.
6. Design, train, and rigorously test machine learning models where needed to improve pricing optimization, focusing on statistical validation and production stability.
7. Implement model performance monitoring (e.g., drift detection, data quality checks) to ensure deployed models maintain accuracy and relevance over time.
8. Collaborate cross-functionally with product, engineering, and data science teams to define SLIs/SLOs for ML services and improve system performance, stability, and usability.
9. 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:

1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.
2. 3+ years of experience in a data engineering or machine learning role, with demonstrated success in MLOps and deploying models to production.
3. Proven expertise in designing and implementing ML testing strategies (e.g., data validation, model correctness, performance testing).
4. Expertise in deploying ML models at scale on AWS, with experience using MLFlow or similar platforms.
5. Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews).
6. Expert-level SQL skills and experience working with large datasets for analysis and modeling.
7. Strong problem-solving skills with the ability to apply creative, data-driven solutions to complex business challenges.
8. Excellent communication and collaboration skills, with experience working cross-functionally with product and engineering teams.

Bonus Skills to Stand Out (Optional):

1. Experience with CI/CD tooling (e.g., GitHub Actions, Jenkins) specifically for ML pipelines and Airflow DAG deployment.
2. Experience with data quality monitoring tools and frameworks.
3. Master’s or PhD in Computer Science, Data Science, or a related field. Relevant certifications (AWS, MLFlow, or other data science/ML certifications).

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