Machine Learning Engineer
Sardine · North America · Engineering
About this role
Sardine is hiring a mid-level Machine Learning Engineer as a remote position. The posting calls out experience with Python, SQL, Kubernetes, Docker and roughly 5+ years of relevant work. Listed education preference: a bachelor's degree or equivalent. Compensation is listed at $170,000–$220,000 per year.
- Role
- Machine Learning Engineer
- Function
- machine learning
- Level
- mid
- Track
- Individual contributor
- Employment
- Full-time
- Location
- North America
- Work mode
- Remote
- Experience
- 5+ years
- Education
- Bachelor's degree
- Department
- Engineering
- Posted
- Sep 9, 2025
More roles at Sardine
Job description
from Sardine careersWho 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, 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.
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.