Senior Applied AI Research Scientist

What you'll do

• Scope and run foundation-model research, experiments, and development; set evaluation standards and monitor model performance.
• Take models from data preparation and pretraining through adaptation, optimization, and deployment for real-time inference.
• Partner with engineering, customers, and legal and compliance teams on production systems, usable risk decisions, explainability, and governance.

What they ask for

• 4+ years in applied machine learning, quantitative modeling, or ML engineering, including pretraining or substantially adapting at least one foundation model and putting it in front of real traffic.
• Hands-on self-supervised pretraining experience, plus practical fine-tuning and adaptation.
• Production experience with model serving, versioning, monitoring, and rollback.
• Strong Python and SQL; comfort preparing very large datasets.

Nice to have

• Background in fraud, AML, payments, credit, or adversarial machine learning; or experience building and evaluating LLM-based agents in production.
• Publications, released models, or open-source contributions in representation learning or sequence modeling; or model risk management experience in regulated finance.

Tools

Python, SQL

AI focus

Applied foundation-model research and development for fraud detection, including self-supervised pretraining, fine-tuning, distillation, quantization, and real-time deployment.