Machine Learning Engineer

Location:

- Remote - UK, Germany, Netherlands, 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 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

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 understanding of models to debug them. You don't need to have trained one recently, but when precision drops you should know the difference between a data problem, a feature pipeline problem, and a model problem

- Experience building tooling other engineers or data scientists actually use, and the judgement to know what should be self-serve and what shouldn't.

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.