Machine Learning Engineer

## About the company Who we are: Sardine is the leading agentic risk platform for fighting financial crime. Our integrated solution unifies data across risk teams to help organizations stop fraud in real time, prevent AI-driven attacks, and automate fraud and AML operations. Sardine's platform is strengthened by one of the fastest-growing fraud consortiums in the market, spanning more than 6 billion profiled devices, 800 million consumers, and 3 million businesses worldwide. Leading companies including FIS, GoDaddy, Intuit, Edward Jones, ZoomInfo, and Checkout.com rely on Sardine to secure and grow trust in their products. Our culture: We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere We hire talented, self-motivated individuals with extreme ownership and high growth orientation. We value performance and not hours worked. We believe you shouldn't have to miss your family dinner, your kid's school play, friends get-together, or doctor's appointments for the sake of adhering to an arbitrary work schedule. 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. ## Nice to have - 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