Job Description

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 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 us to secure and grow trust in their products.

Our culture:

• We have hubs in the Bay Area, NYC, Austin, Toronto, and São Paulo. However, we maintain a remote-first work culture. #WorkFromAnywhere
• 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.

Location:

• Remote - United States or Canada
• 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.

Compensation

• US: Estimated base salary $175K – $220K • Offers Equity
• Canada: Estimated base salary CA$210K – CA$265K • Offers Equity

About The Role

As a Machine Learning Engineer, you’ll do more than build models - you’ll design the systems that make fraud detection possible. You’ll work across modeling, data pipelines, and backend systems (Go) to ensure ML models run reliably, efficiently, and at scale.

This is a chance to combine applied ML with large-scale systems engineering, owning end-to-end solutions that tackle high-stakes, ever-evolving challenges.

What you’ll be doing:

• Build and optimize data pipelines and backend services to process device and behavioral data in real time.
• Develop and deploy ML models for fraud detection, ensuring they run reliably and efficiently in production.
• Turn raw data into production-ready features that feed our fraud detection systems.
• Collaborate with platform and backend engineers to integrate models seamlessly.
• Maintain high standards of security, privacy, and compliance.
• Champion best practices in testing, documentation, and observability.

What you’ll need:

• 5+ years in software engineering, with strong backend experience (Go or Python).
• Hands-on experience with applied ML using large datasets (PyTorch, Scikit-learn, etc.).
• Strong SQL skills and familiarity with relational and non-relational databases.
• Experience with end-to-end ML systems: feature pipelines, model deployment, monitoring, and iteration.
• Excellent communication skills in English, both written and verbal.
• Bachelor's or Master's in Computer Science, Engineering, or a related discipline.

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