Staff Applied ML Engineer - Financial Crime
Core
Lead the evolution of financial crime detection by defining architecture strategy and shipping production neural models for fraud and money laundering at scale.
Role type
Staff Applied ML Engineer (Financial Crime)
Builds
Production ML systems for real-time financial crime risk decisions
Domain
Financial Services / Financial Crime / Deep Learning
Deliverable
production ML models
Required skills
Deep learning fundamentals, architecture strategy, distributed training, ML pipeline orchestration, Python, PyTorch, system optimization (quantization, batching), mentoring
Preferred skills
FinCrime/AML experience, Graph Neural Networks, Foundation model fine-tuning, LLM evaluation
Technologies
PyTorch, Python, Graph Neural Networks, Foundation Models
Responsibilities
Design and ship deep learning models for financial crime detection, define architecture strategy for ML in risk, build reusable end-to-end ML pipelines, evaluate foundation models for transaction representation, partner on model evaluation and causal measurement, mentor engineers and data scientists
Seniority
Staff, hands-on IC with strategic influence
