Description
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!
Responsibilities
- 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.
Requirements
- 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.
Preferred Attributes
- 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.
Benefits
- Hybrid Work Model: This role follows a hybrid schedule, with a minimum of 3 days per week in the office.
- Comprehensive benefits programs
- Opportunities for professional growth and development