Machine Learning Engineer - Sardine

About Sardine

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.

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, while you build and run the platform they deploy onto, along with 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, and 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, found out why, and implemented fixes so it didn't happen again.
• Strong Python skills, solid software engineering fundamentals, testing, code review, and CI/CD discipline.
• Comfort with Kubernetes, containers, a major cloud provider (we're mostly GCP), and infrastructure-as-code.
• Enough understanding of models to debug them. When precision drops, you 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, with the judgment to know what should be self-serve.

Bonus Points

• Domain knowledge in fraud, risk, or cybersecurity.
• Familiarity with CI/CD, Docker, Kubernetes, and modern DevOps frameworks.
• 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
• Remote-first work culture (#WorkFromAnywhere)
• Flexible paid time off and Year-end break
• Health insurance, dental, and vision coverage (US and Canada specific)
• 4% matching in 401k / RRSP (US and Canada specific)
• MacBook Pro delivered to your door
• One-time home office setup stipend
• Monthly meal stipend
• Monthly social meet-up stipend
• Annual health and wellness stipend
• Annual learning stipend