Machine Learning Engineer

Overview

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. We value work/life balance, maintain a flat hierarchy, and pride ourselves on empathy, ambition, unity, and integrity. You will learn fast about cutting-edge tech and work with some of the brightest and nicest people around—check out our blog to see how we help prevent crime and protect the world's biggest online businesses.

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 as code deployments. Google\'s Best Practices for ML Engineering is our bible. Our models are trained to spot multiple types of fraud in real time, with prediction pipelines under strict SLAs; every prediction must be returned in under 300ms. When models are not performing as expected, it\'s the Detection team\'s responsibility to investigate why. The Detection team collaborates closely with the Data Engineering team to design the data architecture and infrastructure that powers our ML systems.

The Role

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 robust, high-performance ML systems that form the core of our fraud detection platform. You will not only consume data but also define how data is modeled, stored, and served for machine learning purposes. This includes shaping 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 a great opportunity to apply your software engineering expertise to complex ML challenges in a collaborative environment.

Responsibilities

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• 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
  

Requirements

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• Hands-on experience building and deploying machine learning models in a production environment
  
• Solid understanding of the full machine learning lifecycle, including designing 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
  

Nice to Have

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• 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

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• 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
  
• Monthly Wellbeing and Learning Day — Take the last Friday of each month off to recharge or learn something new
  
• 25 Days Holiday + Bank Holidays + 1 Extra Cultural Day
  
• Mental Health Support via Spill — Access professional mental health services
  
• Aviva Pension Scheme — Plan for the future with our pension program
  
• Ravelin Gives Back — Monthly charitable donations and volunteer opportunities
  
• Fortnightly Randomised Team Lunches — Connect with teammates over lunches
  
• Cycle-to-Work Scheme — Save on commuting costs
  
• BorrowMyDoggy Access — Spend time with a dog through this perk
  
• Weekly Board Game Nights & Social Budget — Weekly games or socials supported by a company budget
  
• Pre-employment checks: unspent criminal convictions, employment verification, and right to work
  

Senioritiy level

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• Mid-Senior level
  

Employment type

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• Full-time
  

Job function

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• Other
  

Industries

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• IT Services and IT Consulting
  

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