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.

- 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

- 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.

- 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.

- 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.

To learn more about how we process your personal information and your rights in regards to your personal information as an applicant and Sardine employee, please visit our Applicant and Worker Privacy Notice https://www.sardine.ai/applicant-and-worker-privacy-notice.

Compensation: $175K – $220K • Offers Equity • Multiple Ranges