Machine Learning Engineer, Fraud

📋 Description

• Design, train, and deploy ML/LLM models to detect fraudulent behaviors.
• Lead end-to-end fraud detection architecture.
• Build graphs to model behavioral patterns and account connectivity.
• Develop scalable data pipelines and real-time ML inference.
• Conduct deep behavioral and adversarial analysis to improve detection.
• Collaborate with Trust & Safety, Payments, and Infra on features and evaluation.

🎯 Requirements

• Bachelor's degree in CS or related field, or equivalent.
• 2-6 years in ML or software engineering, ideally in fraud/trust domains.
• Strong Python and ML libraries (scikit-learn, PyTorch, LightGBM).
• Backend development and deploying ML models to production.
• Experience with SQL, Spark, DBT for ETL/data pipelines.
• Familiarity with fraud techniques (chargeback, anomaly, graph) and orchestration tools (Dagster, Kubeflow).

🎁 Benefits

• Generous Holiday and Time off Policy
• Health Insurance options (Medical, Dental, Vision)
• Work From Home Support and Home Office Setup Allowance
• Monthly allowance for cell phone and internet
• Care benefits (wellness, childcare, family planning)
• Retirement: 401k and international pension plans