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At a glance
- Employment type
- Full-time
- Location
- Dublin, Ireland
- Workplace
- On-site
- Category
- AI
Technologies & skills
What you'll be doing
CarTrawler seeks a Machine Learning Engineer in Dublin to build and enhance Data Science solutions. You'll collaborate with engineers and business teams, support ML deployments, maintain platforms, write clean Python code, and explore new technologies. Opportunity to work with cloud and container tech in a collaborative environment.
- Build and improve tools and services for Data Science solutions using Python, AWS, Docker, and Kubernetes
- Collaborate with engineers, data scientists, and business teams to develop technical solutions
- Support development and deployment of machine learning solutions, including delivery pipelines, testing, monitoring, and performance improvements
- Maintain and enhance existing Data Science platforms, tools, and Python libraries
- Write clean, maintainable code and participate in code reviews
- Explore new ideas and technologies, contribute to proof-of-concept projects, and share learnings
Key requirements
Must-have
- Foundation in software engineering via degree or relevant experience
- Confidence writing clean, structured Python code
- Familiarity with data science and machine learning libraries
- Understanding of modern development practices including Git, automated testing, OOP, and CI/CD
- Exposure to cloud and container technologies such as AWS, Azure, Docker, or Kubernetes
- Basic SQL skills and understanding of APIs
- Curious, proactive, and collaborative mindset with strong communication skills
Role signals
- Technical focus
- machine learning engineering
- Hands-on vs management
- Hands-on
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Full job description
Tech that takes you places
We’re CarTrawler, the global travel tech company behind seamless connections between people, places and possibilities. From powering car rental to creating smarter ways to move, we make travel smoother for millions worldwide. Our culture is built on curiosity, collaboration and craic where every idea counts and every journey matters. Ready to make an impact? Let’s go places together.CarTrawler’s Revenue team sits at the intersection of data, strategy and opportunity. Through dynamic pricing, forecasting and optimisation, we maximise performance across channels and markets. Our work balances analytical precision with creative experimentation turning insights into actions that drive sustainable profitability.
Your Journey starts here!
Love solving technical challenges and turning innovative ideas into real-world solutions? We’re looking for a curious and collaborative Machine Learning (ML) Engineer to help build and enhance the technology behind our Data Science solutions. You’ll work alongside experienced engineers and cross-functional teams, developing reliable tools, supporting machine learning deployments and helping our technology perform at its best. With opportunities to explore modern cloud technologies, contribute to meaningful projects and grow your engineering skills, this is a chance to make an impact while learning from a talented team.
What you’ll do as an ML Engineer
Build and improve the tools and services that power our Data Science solutions, using technologies such as Python, AWS, Docker and Kubernetes.
Collaborate with engineers, data scientists and business teams to understand challenges, explore opportunities and develop practical technical solutions.
Support the development and deployment of machine learning solutions, contributing to automated delivery pipelines, testing, monitoring and performance improvements.
Maintain and enhance existing Data Science platforms, tools and Python libraries to keep our systems reliable, efficient and scalable.
Write clean, maintainable code and participate in code reviews, applying engineering best practices with support from experienced colleagues.
Explore new ideas and technologies, contribute to proof-of-concept projects and share progress and learnings with colleagues across the business.
What you’ll bring to the team
A foundation in software engineering, gained through a degree in Computer Science, Engineering, Mathematics or a related field, or relevant internship or industry experience.
Confidence writing clean, structured Python code, with familiarity with libraries used in data science and machine learning.
An understanding of modern development practices, including version control with Git, automated testing, object-oriented programming and continuous integration and delivery.
Exposure to cloud and container technologies, such as AWS, Azure, Docker or Kubernetes, alongside an interest in building reliable, scalable solutions.
An understanding of data and software integration, including basic SQL skills, application programming interfaces (APIs) and a willingness to develop your knowledge of production systems.
A curious, proactive and collaborative mindset, with strong communication skills, a willingness to learn from others and enthusiasm for solving problems together.
Interview prep pack
Grounded in this listing. Use it to prepare examples before you apply.
Your interview focus
Based on this listing, the Machine Learning Engineer role at CarTrawler focuses on building and improving data science tools, supporting ML solution deployment, and collaborating across teams using Python, cloud, and container technologies.
- Hands-on ML engineering with Python and cloud technologies·High
- Collaboration and communication·High
- Software engineering best practices·Medium
Only have 30 minutes?
Follow a focused preparation plan based on this job.
Start 30-minute prep
Your 30-minute plan
Review Python and Data Science Libraries
0–8 minBrush up on Python syntax, best practices, and commonly used data science libraries such as pandas, NumPy, and scikit-learn.
Refresh Cloud and Container Knowledge
8–15 minStudy the basics of AWS, Docker, and Kubernetes, focusing on their use in ML workflows and deployment.
Prepare Examples of Collaboration and Problem-Solving
15–20 minIdentify 1-2 strong examples from your experience where you worked with others to solve technical challenges.
Review Software Engineering Practices
20–25 minRevisit your experience with Git, automated testing, CI/CD, and code reviews to discuss confidently.
Research CarTrawler and the Revenue Team
25–30 minLearn about CarTrawler’s business, culture, and the role of the Revenue team to tailor your questions and answers.
Talking points
, 5 itemsPython for Data Science and ML
Demonstrate your ability to write clean, maintainable Python code and familiarity with libraries commonly used in data science and machine learning.
Cloud and Containerization Experience
Showcase your exposure to AWS, Docker, Kubernetes, or similar technologies, and how you've used them to build or deploy scalable solutions.
Software Engineering Best Practices
Highlight your understanding of version control, automated testing, object-oriented programming, and CI/CD pipelines, as these are core to the role.
Collaboration and Communication
Prepare examples of working with engineers, data scientists, or business teams to solve technical challenges and deliver practical solutions.
SQL and API Integration
Be ready to discuss your experience with basic SQL and integrating data or services via APIs, as these are mentioned as important skills.
What to research
, 4 itemsCarTrawler's Data Science and Revenue Teams
Research CarTrawler's approach to data science, dynamic pricing, and how the Revenue team contributes to business outcomes.
Python Data Science Ecosystem
Review key Python libraries for data science and machine learning (e.g., pandas, NumPy, scikit-learn) and be ready to discuss your experience.
Cloud and Container Technologies
Refresh your knowledge of AWS, Docker, and Kubernetes, focusing on how they are used in deploying and scaling ML solutions.
Modern Software Engineering Practices
Prepare to discuss your experience with Git, automated testing, CI/CD, and code reviews.
Questions to ask
, 6 itemsHow does the Revenue team collaborate with other departments to drive business impact?
Why ask this? To understand the cross-functional nature of the role and how your work will contribute to company goals.
What are the main challenges currently faced in deploying and maintaining machine learning solutions at CarTrawler?
Why ask this? To gain insight into the technical and operational hurdles you may encounter.
How does the team approach experimentation and proof-of-concept projects?
Why ask this? To learn about the company's openness to innovation and your potential to contribute new ideas.
What opportunities are there for professional development and learning new technologies within the team?
Why ask this? To assess how the company supports growth and continuous learning.
How is success measured for ML engineers in this role?
Why ask this? To clarify expectations and performance metrics.
What is the typical workflow for deploying a new machine learning model into production?
Why ask this? To understand the end-to-end process and your role within it.
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