Machine Learning Engineer Language

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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 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.
For more information check out our blog to see if you would like to help us prevent crime and protect the world's 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. 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 productionize 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.
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.
Software engineering fundamentals, including data structures, design patterns, version control (Git), CI/CD, testing, and monitoring.
Proficiency in a systems programming language (e.g., Go, C++, Java, Rust).
Experience with large-scale data processing engines like Spark and Dataproc.
Familiarity with data pipeline tools like dbt.
Flexible Working Hours & Remote-First Environment - Work when and where you're most productive, with flexibility and support.
~ Comprehensive BUPA Health Insurance - Stay covered with top-tier medical care for your peace of mind.
~25 Days Holiday + Bank Holidays + 1 Extra Cultural Day - Enjoy generous time off to rest, travel, or celebrate what matters to you.
~ 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.
~ Fortnightly Randomised Team Lunches - Connect with teammates from across the company over in person or remote lunches every other week, on us!
~ Cycle-to-Work Scheme - Save on commuting costs while staying active.
~ BorrowMyDoggy Access - Love dogs? Weekly Board Game Nights & Social Budget - Unwind with weekly board games or plan your own socials, supported by a company budget.

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Location:
London
Job Type:
FullTime

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