Senior AI / Machine Learning Engineer — Fraud Prevention

What you'll do

• Build and deploy ML models for fraud, abuse, anomaly detection, and risk scoring; engineer signals from account, device, network, behavioral, velocity, and session data.
• Integrate ML/AI into real-time risk decisioning and enforcement, and apply LLMs and AI agents to detection, investigation, and classification.
• Evaluate, monitor, and improve production solutions as attacks evolve; collaborate across engineering, product, and risk teams.

What they ask for

• 8+ years building and operating production ML systems; ideally in fraud, abuse, risk, identity, trust & safety, or other adversarial domains.
• Practical experience with Python, SQL, and current ML frameworks such as PyTorch.
• Experience guiding ML systems from feature engineering through production deployment and monitoring, with strong software/data engineering skills.
• Experience building with LLMs and/or AI agents, particularly for AI/generation-abuse use cases.

Nice to have

• Device fingerprinting, identity verification, behavioral signals, network intelligence, or VPN/proxy detection.
• Real-time risk evaluation, automated control systems, human-in-the-loop or AI-assisted evaluation, or distributed systems and high-scale data pipelines.

Pay

$151,800 - $265,350 annually (base salary); $183,300 - $265,350 (base salary); $183,300 - $265,350 (base salary); $156,300 - $226,350 (base salary); $165,600 - $239,725 (base salary)

Tools

Python, SQL, PyTorch, LLMs, AI agents

AI focus

Applies LLMs and AI agents to fraud and abuse detection, investigation, and classification, including AI/generation-abuse use cases.