Overview
We are Ravelin, a fraud detection company using 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. We value empathy, ambition, unity, and integrity, and we pride ourselves on a strong culture with work/life balance and a flat hierarchy. 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.
The Team
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.
The Role
We are looking for a Senior Machine Learning Engineer to join our Detection team. In this role, you will be setting the technical direction that bridges data science and engineering. You will be responsible for the architecture, scalability, and reliability of the high-performance ML systems that form the core of our fraud detection platform. Beyond just consuming data, you will take a leading role in defining how data is modeled, stored, and served for machine learning purposes, directly influencing the architecture of our feature generation pipelines and ensuring data quality throughout the ML lifecycle. You'll take strategic ownership over several aspects of our ML infrastructure and be empowered to introduce and champion new ideas that shape the future of our processes and tools. Your day-to-day will involve close collaboration with engineers and data scientists to operate machine learning at scale, while also providing mentorship and guidance to other members of the team.
Responsibilities
- Lead the design, architecture, and orchestration of 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
- Propose and champion new machine learning methods and tools to influence the technical roadmap and drive continuous innovation
- Drive cross-functional initiatives with Data Engineering, Infra, and other teams to align on data architecture and ensure our ML systems meet overarching business objectives
- Evolve our MLOps infrastructure, driving the strategy for model versioning, automated deployments, monitoring, and observability using modern tools like Prefect
- Mentor and guide other members of the team, fostering a culture of technical excellence and continuous improvement through code reviews, design discussions, and knowledge sharing
- Lead technical deep-dives to troubleshoot and resolve performance bottlenecks and availability issues in our ML systems
- Champion and contribute to the continuous improvement of our internal tools and engineering best practices
Requirements
- Demonstrable experience designing, building, and deploying complex machine learning systems in a production environment
- Deep understanding of the full machine learning lifecycle, from research to deployment and a track record of leading the design and implementation of scalable training pipelines for large datasets
- Working experience leading complex, cross-functional projects and influencing technical direction across multiple teams
- Familiarity with modern 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
- Exceptional problem-solving skills, with a proven ability to navigate ambiguity and lead technical deep-dives to resolve complex issues
- A collaborative mindset and strong communication skills with the ability to communicate to a range of audiences.
Nice to Have
- 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
Benefits
- Flexible Working Hours & Remote-First Environment — Work when and where you're most productive, with flexibility and support
- Comprehensive BUPA Health Insurance — Top-tier medical care for your peace of mind
- £1,000 Annual Wellness and Learning Budget — Funds for fitness, mental health, and learning needs
- Monthly Wellbeing and Learning Day — Take every last Friday of the month off to recharge or learn something new
- 25 Days Holiday + Bank Holidays + 1 Extra Cultural Day — Generous time off to rest, travel, or celebrate
- Mental Health Support via Spill — Access professional mental health services when you need them
- Aviva Pension Scheme — Plan for the future with our pension program
- Ravelin Gives Back — Monthly charitable donations and volunteer opportunities
- Fortnightly Randomised Team Lunches — In-person or remote lunches every other week
- Cycle-to-Work Scheme — Save on commuting costs while staying active
- BorrowMyDoggy Access — Spend time with a dog through this perk
- Weekly Board Game Nights & Social Budget — Unwind with board games or socials
- Pre-employment checks notice — Job offers may be withdrawn if candidates do not meet our pre-employment checks: unspent criminal convictions, employment verification, and right to work
Industries
- IT Services and IT Consulting
Seniority Level
Employment Type
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