Machine learning Engineer Our Client Sardine is a leader in financial crime prevention, utilizing advanced device intelligence and behavior biometrics combined with machine learning to detect and prevent fraud before it occurs.
Their platform offers tools for identity verification, fraud prevention and investigation, AML monitoring, and case management.
Over 250 companies rely on Sardine to combat issues such as fake account creation, social engineering scams, account takeovers, bot attacks, payment fraud, and money laundering.
Sardine has secured $75M in funding from prominent investors including Andreessen Horowitz Growth, XYZ Capital, Google Ventures, Visa, Activant Capital, Experian, and ING Ventures.
Sardine maintains a remote-first work culture with hubs in the Bay Area, NYC, Austin, and Toronto.
The company emphasizes hiring talented, self-motivated individuals and values performance over hours worked.
They support a flexible work environment, allowing employees to balance work with personal commitments like family dinners, school events, and medical appointments.
Why our client is hiring for this position/mission The client is seeing more and more demand for Snowflake and to meet them in the best way, they will build a snowflake application using data modeling Team You'll report to the head of engineering and will collaborate closely with ML platform team Requirements 3+ years of experience working in Machine Learning or similar roles Extensive knowledge about data warehousing Extensive knowledge in computer science, machine learning, and statistics Strong programming skills in Python and SQL Experience managing the machine learning lifecycle Desirable Experience in Industry (very nice to have): Fraud / Risk / Compliance / Payments / FinTech Familiarity with Kubernetes or Docker for containerization is nice to have Startup experience is a plus Knowledge of snowflake and snowflake ML would be a plus Timezone Brazil and Argentina Recruitment Process Technical Conversation - 1 hour Please notice that changes can happen.
Steps could be added/skipped depending on the candidate's performance/team's availability.
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