Applied AI Research Scientist

Overview

Applied AI Research Scientist to drive foundation model research and practical, state-of-the-art fraud detection solutions using deep learning and foundation models.

What you'll do

• Identify and scope opportunities, design experiments, and execute the foundation model research and development roadmap.
• Own foundation model evaluation with offline benchmarks and monitoring for calibration, drift, and degradation.
• Develop end-to-end model pipelines from data preparation through pretraining, fine-tuning, distillation, quantization, and deployment.
• Partner with Engineering on training infrastructure, GPU efficiency, feature/embedding stores, and production serving.
• Work with customer-facing teams to translate model capabilities and limits into risk-team decisions.
• Partner with Legal, Compliance, and model risk teams on explainability, documentation, and governance.

What you'll need

• 4+ years in applied machine learning, quantitative modeling, or ML engineering including at least one foundation model pre-trained or substantially adapted and exposed to real traffic.
• Hands-on self-supervised pretraining experience plus practical fine-tuning and adaptation.
• Production experience with model serving, versioning, monitoring, and rollback.
• Ability to self-manage ambiguous applied research with clear communication across partner teams.
• Strong Python and strong SQL, including comfort preparing very large datasets.

Nice to have

• Background in fraud, AML, payments, credit, or adversarial machine learning.
• Experience building and evaluating LLM-based agents in production.
• Publications, released models, or open-source contributions in representation learning or sequence modeling.
• Experience with model risk management and documentation in a regulated financial environment.

Details

• Work location: Remote in the United States or Canada.