Machine Learning Engineer, Radar

What you'll do

• Build, train, evaluate, deploy, and monitor fraud and abuse detection models.
• Investigate emerging attack patterns and develop features, datasets, evaluations, and experiments to improve model performance.
• Partner across disciplines to turn modeling improvements into Radar products and improve production ML tools and workflows.

What they ask for

• 2+ years of experience developing, evaluating, and deploying machine learning models in a production environment.
• Strong foundations in machine learning, data analysis, statistics, model evaluation, and experiment design.
• Strong programming and software-engineering skills, including tested, maintainable code for production ML applications.
• Experience building or operating production ML systems, including one or more of large-scale data and feature pipelines, distributed model training, low-latency inference, model deployment, or production monitoring.

Nice to have

• Experience owning an ML problem across its full lifecycle.
• Experience with modern deep learning, graph-learning, or LLM-based approaches; real-time ML at scale; or ML in adversarial domains.

Tools

transformer architectures, LLMs

AI focus

Develop and deploy ML approaches for fraud and abuse detection, potentially including transformers for payment data, graph-powered intelligence, LLMs to detect emerging attacks, and AI-assisted workflows.