Senior AI​/Machine Learning Engineer - Fraud Detection

Senior AI / Machine Learning Engineer - Fraud Detection The Opportunity

We are in search of an experienced Senior AI/ML Engineer to develop and broaden fraud and abuse detection systems. You will improve ML techniques in anomaly detection, user/device risk analysis, identity and service abuse, and evolving AI abuse scenarios.

This is a hands-on role spanning ML, data, and backend systems, with opportunities to apply LLMs, AI agents, and modern ML techniques to strengthen detection. You will own solutions end to end - from signals and modeling through production deployment and real-time decisioning. Join us in crafting best in class fraud detection systems that will make a significant impact!

• Build and deploy high-precision ML models for fraud and abuse detection, anomaly detection, and risk scoring.
• Engineer risk signals from large-scale account, device, network, behavioral, velocity, and session data.
• Integrate ML/AI features into real-time risk decisioning and automated enforcement systems.
• Apply LLMs and AI agents to expand detection, investigation, and classification capabilities.
• Translate emerging attack patterns and relevant research into new models, signals, and mitigations.
• Evaluate solutions across accuracy, latency, cost, and customer impact.
• Own model evaluation, monitoring, and drift as attacker behavior evolves.
• Partner across engineering, product, and risk teams to ship production-ready capabilities.

• 8+ years building and operating production ML systems, ideally in fraud, abuse, risk, identity, trust & safety, or other adversarial domains.
• Solid ML background with practical experience in Python, SQL, and current ML frameworks like PyTorch.
• Experience guiding ML systems from feature engineering to production deployment and monitoring.
• Strong software/data engineering skills across ML, backend, and data infrastructure.
• Experience building with LLMs and/or AI agents, particularly for AI/generation-abuse use cases.
• Strong technical judgment, ownership, and ability to solve ambiguous, adversarial problems.
• Bachelor's or equivalent experience in Computer Science, Statistics, Mathematics, or related field; advanced degree a plus.

• Device fingerprinting, identity verification, behavioral signals, network intelligence, or VPN/proxy detection.
• Real-time risk evaluation and automated control systems.
• Human-in-the-loop or AI-assisted evaluation systems.
• Distributed systems and high-scale data pipelines.
• Strong adversarial approach - anticipating how attackers adapt to mitigations.

Hybrid Work Model:
This role follows a hybrid schedule, with a minimum of 3 days per week in the office.

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