At a Glance
- Tasks: Design systems for fraud detection and optimise data pipelines using machine learning.
- Company: Remote-first company with a globally distributed team, offering flexibility.
- Benefits: Generous compensation, flexible time off, health coverage, and stipends for home office setup.
- Other info: Opportunity for career growth in a dynamic, innovative environment.
- Why this job: Make a real impact in fraud detection while working from anywhere you choose.
- Qualifications: Experience in applied ML, strong SQL skills, and a degree in Computer Science or related field.
The predicted salary is between 70000 - 90000 £ per year.
Location: Remote - UK, Germany, Ireland, Spain, Poland, Bulgaria or Lithuania. From Home / Beach / Mountain / Cafe / Anywhere! We are a remote-first company with a globally distributed team. You can find your productive zone and work from there.
About the Role: As a Machine Learning Engineer, you’ll do more than build models - you’ll design the systems that make fraud detection possible. You’ll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale. This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges.
What you’ll be doing:
- Build and optimize data pipelines and backend services to process device and behavioral data in real time.
- Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.
- Turn raw data into production-ready features that feed our fraud detection systems.
- Collaborate with platform and backend engineers to integrate models seamlessly.
- Maintain high standards of security, privacy, and compliance.
- Champion best practices in testing, documentation, and observability.
What you’ll need:
- Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).
- Strong SQL skills and familiarity with relational and non-relational databases.
- Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration.
- Excellent communication skills in English, both written and verbal.
- Bachelor’s or Master’s in Computer Science, Engineering, or a related discipline.
Bonus Points:
- Domain knowledge in fraud, risk, or cybersecurity.
- Background in Software Engineering.
- Familiarity with CI/CD, Docker, Kubernetes and the modern devops framework.
- Understanding of modern browser APIs and high-entropy data collection techniques.
- Familiarity with leveraging frontier LLMs for automation.
Benefits we offer:
- Generous compensation in cash and equity.
- Early exercise for all options, including pre-vested.
- Work from anywhere: Remote-first culture.
- Flexible paid time off and year-end break.
- Health insurance, dental, and vision coverage for employees and dependents - US and Canada specific.
- 4% matching in 401(k) / RRSP - US and Canada specific.
- MacBook Pro delivered to your door.
- One-time stipend to set up a home office — desk, chair, screen, etc.
- Monthly meal stipend.
- Monthly social meet-up stipend.
- Annual health and wellness stipend.
- Annual learning stipend.
Machine Learning Engineer United Kingdom employer: Sardine
As a remote-first company, we offer unparalleled flexibility, allowing you to work from your ideal environment—be it a beach, mountain, or café. Our culture prioritises collaboration and innovation, providing generous benefits such as competitive compensation, stipends for home office setup, and opportunities for continuous learning and personal growth. Join us to tackle high-stakes challenges in fraud detection while enjoying a supportive and dynamic work atmosphere.
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We think this is how you could land Machine Learning Engineer United Kingdom
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We think you need these skills to ace Machine Learning Engineer United Kingdom
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Craft a Tailored Cover Letter:For a full-time role at Sardine, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Sardine. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
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✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
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Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
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✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
