Machine Learning Engineer

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 - United States or Canada 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 at Sardine, you'll own the systems that make real [... source excerpt omitted ...] b-250ms budget. That constraint shapes everything: how features are computed and served, how models are deployed and rolled back, how quickly you know when something has degraded. You'll be the person who figures out why a model broke. What you'll be doing: Build and own the model serving infrastructure, real-time inference, feature retrieval, and the latency budget that governs both Build the deployment path our data scientists use to ship models themselves, including bring-your-own-model support for clients hosting their own Own models in production: monitoring, drift detection, retraining, incident response, and the on-call rotation Build and optimise the pipelines that [... source excerpt omitted ...] d models yourself where it makes sense, roughly 20% of the role, and more if you want it Champion testing, observability, security and compliance in a regulated environment What you'll need Experience building, not just using, model serving infrastructure. Production ownership of ML systems: you've been paged when something broke, you found out why, and you changed something so it didn't happen again. Strong Python, and solid software engineering fundamentals, testing, code review, CI/CD, the discipline that makes a platform other people can rely on. Comfort with Kubernetes, containers and a major cloud (we're mostly GCP), plus infrastructure-as-code. Enough understandin