Who we are:
We are a leader in fraud prevention and AML compliance. Our platform uses device intelligence, behavior biometrics, machine learning, and AI to stop fraud before it happens. Today, over 300 banks, retailers, and fintechs worldwide use Sardine to stop identity fraud, payment fraud, account takeovers, and social engineering scams. We have raised $145M from world-class investors, including Andreessen Horowitz, Activant, Visa, Experian, FIS, and Google Ventures.
Our culture:
We have hubs in the Bay Area, NYC, Austin, and Toronto. 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.
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 Do
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 Bring
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.
Compensation: Base pay range of $170,000 - $200,000 USD / $197,000 - $270,000 CAD + equity with tremendous upside potential + Attractive benefits
The compensation offered for this role will depend on various factors, including the candidate's location, qualifications, work history, and interview performance, and may differ from the stated range.
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, Year-end break, Self care days off
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
Unlimited access to an expert financial advisory
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.
