Staff Applied ML Engineer - Financial Crime
Core
Designing and shipping deep learning models for real-time financial crime detection, defining architecture strategy, and building reusable ML pipelines for fraud and money laundering patterns.
Role type
Staff Applied ML Engineer (Financial Crime)
Builds
Production neural models, end-to-end ML pipelines, and architecture blueprints for FinCrime domains.
Domain
Financial services / Financial crime / Deep learning
Deliverable
production ML models
Required skills
Deep learning fundamentals, architecture-level decision making, distributed training, ML pipeline orchestration, Python, PyTorch, graph neural networks, sequence modelling, attention mechanisms, model optimization (quantization, batching), real-time system design
Preferred skills
FinCrime/fraud detection/AML experience, foundation model fine-tuning, LLM evaluation, establishing modern ML practices
Technologies
PyTorch, Python, Graph Neural Networks, Foundation Models
Responsibilities
Designing and shipping ML and deep learning models for financial crime detection, Defining the architecture strategy for applying modern ML to risk, Building the reusable end-to-end pipeline pattern, Evaluating and prototyping foundation model approaches, Partnering with Data Science on model evaluation and experimentation, Mentoring engineers and data scientists on modern ML fundamentals
Seniority
Staff, hands-on IC with strategic influence
