Remote Computer Science Graduate Jobs in Missouri

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:

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 scale on AWS, with experience using MLFlow, Sagemaker or similar platforms.
• Strong Python programming skills and adherence to software engineering best practices (e.g., clean code, version control, code reviews, using Docker, Terform, Kubernetes).
• 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.
• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field.

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, Mathematics, or a related field. 

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