Software Engineer II, Machine Learning, Risk Engineering
$153,000–$199,000 year
Hybrid · Brooklyn, New York, United States
Job Summary
Software Engineer II in Machine Learning for Risk Engineering focuses on prototyping, optimizing, and productionizing large-scale ML models to detect and prevent fraud, with opportunities to publish and present research. The role collaborates with product managers and engineers to protect millions of users, uses models like GBDT and DNNs, conducts AB tests, and contributes to trust and safety initiatives. Flexible work modes exist with in-office attendance once or twice per week for employees located in Brooklyn (NYC) or the San Francisco Bay Area.
Required Qualifications
- Ph.D. degree in a quantitative field (e.g., computer science, industrial engineering, applied math, statistics) with machine learning research experience on risk/fraud applications, or a MS degree in related fields and 3+ years of industry experience in risk/fraud applications
- publication at peer-reviewed conferences (e.g., ICML, KDD, SIGIR, WSDM) or talks/tutorials in industry conferences
- experience deploying, debugging, and improving machine learning models in large-scale production systems in public clouds (e.g., GCP, AWS, or Azure) with Infrastructure as Code (Terraform)
- experience or interest in building production risk/fraud detection systems or general e-commerce systems
- ability to work with product managers, ML engineers, full-stack engineers, and designers
- strong communication and collaboration skills
- willingness to commute to Brooklyn, NY or SF Bay Area and work in-office 1-2 days per week
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