Staff Machine Learning Engineer

We are seeking an exceptional Staff Machine Learning Engineer to lead the design and development of the next generation of our AI-driven fraud detection platform .

You will architect large-scale ML systems that detect and prevent fraud in real time combining deep machine learning expertise with scalable engineering and domain knowledge in financial systems.

This is a hands-on technical leadership role, shaping our fraud prevention roadmap and ensuring the platform evolves to meet emerging threat patterns through automation, data intelligence, and generative AI–enhanced detection models.

Responsibilities

• Architect and build scalable ML systems for fraud detection, anomaly detection, and behavioral analysis.
• Develop and maintain end-to-end ML pipelines : data ingestion, feature engineering, model training, deployment, and monitoring.
• Leverage modern AI techniques , including generative AI, to improve fraud pattern discovery and model robustness.
• Design and implement real-time decision systems , integrating with transaction or behavioral data streams.
• Collaborate closely with engineering, security, and risk teams to define data strategy and labeling frameworks.
• Lead experimentation on model explainability, drift detection, and adversarial robustness for fraud prevention use cases.
• Promote engineering excellence — automation, CI / CD, reproducibility, observability, and model governance.
• Mentor and guide ML and software engineers, fostering best practices and innovation.
• 5+ years of experience building ML or AI systems in production; at least 2+ in fraud, risk, or anomaly detection domains.
• Proven track record designing and maintaining ML pipelines at scale.
• Expertise in Python , ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and CI / CD (GitHub Actions, Jenkins, or similar).
• Strong understanding of supervised / unsupervised learning , anomaly detection, and statistical modeling.
• Experience with big data and distributed systems (e.g., Spark, Kafka, Flink, or similar).
• Familiarity with cloud platforms (AWS, GCP, or Azure) and containerized deployments (Docker, Kubernetes).
• Strong collaboration, communication, and cross-team leadership skills.

Preferred Qualifications