Machine Learning Engineer

Who we are

The company 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. The company’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 the company 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 the company, 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.

The company 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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