Hi! We are Ravelin! We're a fraud detection company using advanced machine learning and network analysis technology to solve big problems. Our goal is to make online transactions safer and help our clients feel confident serving their customers.
And we have fun in the meantime! We are a friendly bunch and pride ourselves in having a strong culture and adhering to our values of empathy, ambition, unity, and integrity. We really value work/life balance and we embrace a flat hierarchy structure company-wide. Join us and you’ll learn fast about cutting-edge tech and work with some of the brightest and nicest people around - check out our Glassdoor reviews.
If this sounds like your cup of tea, we would love to hear from you! For more information check out our blog to see if you would like to help us prevent crime and protect the worlds biggest online businesses.
You will be joining the Detection team, a team of data scientists and machine learning engineers. The Detection team is responsible for keeping fraud rates low – and clients happy – by continuously training and deploying machine learning models. We aim to make model deployments as easy and error-free as code deployments. Google’s Best Practices for ML Engineering is our bible.
Our models are trained to spot multiple types of fraud, using a variety of data sources and techniques in real time. The prediction pipelines are under strict SLAs; every prediction must be returned in under 300ms. When models are not performing as expected, it’s down to the Detection team to investigate why.
The Detection team is core to Ravelin’s success. They work in a deeply collaborative partnership with the Data Engineering team to design the data architecture and infrastructure that powers our ML systems. This close alignment ensures our models are built on a foundation of high-quality, reliable, and efficiently processed data.
We are looking for a Machine Learning Engineer to join our Detection team. You will be the crucial bridge between data science and engineering, responsible for productionising the cutting-edge models our data scientists develop. Your role is to build, scale, and maintain the robust, high-performance ML systems that form the core of our fraud detection platform. You will not only consume data but also play a critical role in defining how data is modeled, stored, and served for machine learning purposes. This includes influencing the architecture of our feature generation pipelines and ensuring data quality is paramount throughout the ML lifecycle
You'll have ownership over our ML infrastructure and be empowered to introduce new ideas that enhance our processes and tools. Your day-to-day will involve close collaboration with engineers and data scientists to operate machine learning at scale. This is the perfect opportunity to apply your software engineering expertise to complex machine learning challenges and grow within a collaborative and innovative environment.
- Design, build, and orchestrate scalable and reliable end-to-end ML pipelines – from raw data extraction and feature engineering to model training and inference – with a focus on handling terabyte-scale datasets efficiently
- Collaborate with Data Scientists to productionise new machine learning models, ensuring they are performant, scalable, and maintainable.
- Implement and manage the orchestration of complex, multi-stage ML jobs using modern workflow orchestration tools like Prefect.
- Enhance and manage our MLOps infrastructure, including model versioning, automated deployments, monitoring, and observability.
- Troubleshoot and resolve performance bottlenecks and availability issues in our production ML systems.
- Contribute to the continuous improvement of our internal tools and engineering best practices.
- Hands-on experience building and deploying machine learning models in a production environment.
- Solid understanding of the full machine learning lifecycle, from research to deployment and experience with designing and implementing scalable training pipelines for large datasets.
- Familiarity with workflow orchestration tools such as Prefect, Kubeflow, Argo, etc.
- Software engineering fundamentals, including data structures, design patterns, version control (Git), CI/CD, testing, and monitoring.
- Excellent problem-solving skills and the ability to work through ambiguous requirements.
- A collaborative mindset and strong communication skills with the ability to communicate to a range of audiences.
- Proficiency in a systems programming language (e.g., Go, C++, Java, Rust).
- Experience with deep learning frameworks like PyTorch or TensorFlow.
- Experience with large-scale data processing engines like Spark and Dataproc.
- Familiarity with data pipeline tools like dbt.
- Flexible Working Hours & Remote-First Environment
- Comprehensive BUPA Health Insurance
- £1,000 Annual Wellness and Learning Budget
- Monthly Wellbeing and Learning Day
- 25 Days Holiday + Bank Holidays + 1 Extra Cultural Day
- Mental Health Support via Spill
- Aviva Pension Scheme
- Ravelin Gives Back
- Fortnightly Randomised Team Lunches
- Cycle-to-Work Scheme
- BorrowMyDoggy Access
- Weekly Board Game Nights & Social Budget
Job offers may be withdrawn if candidates do not meet our pre-employment checks: unspent criminal convictions, employment verification, and right to work