Machine Learning Engineer

About the role

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 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.

- We're a remote-first team spread across time zones, so no office to report to - work from wherever helps you do your best work. Just a couple of things to keep in mind: pay is based on where you're located, and you'll need to keep a home base in the country you're hired in. So while we love the "coffee shop today, mountains tomorrow" life, this isn't a passport-optional, work-from-anywhere-on-Earth kind of remote - think flexible within your country, not borderless.

Location:

- Remote - United States or Canada

About the role:

As a Machine Learning Engineer at Sardine, you'll own the systems that make real-time fraud detection possible. Our data science team builds custom models for our clients, you build and run the platform they deploy onto, and the low-latency serving path those models score on. Sardine scores millions of sessions in real time from hundreds of device and behavioural signals, inside a sub-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 turn raw device and behavioural signals into production-ready features

- Work across Python and our Go backend to keep inference fast inside the request path

- Build 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