Machine Learning Engineer

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.

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 401k / 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

Join a fast-growing company with world-class professionals from around the world. If you are seeking a meaningful career, you found the right place, and we would love to hear from you.

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