Staff Applied ML Engineer - Financial Crime
Core
Lead the architecture strategy and production deployment of deep learning models for real-time financial crime detection at scale.
Role type
Staff Applied ML Engineer (Financial Crime)
Builds
Production neural models, end-to-end ML pipelines, and reusable patterns for risk modelling.
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), graph neural networks, sequence modelling
Preferred skills
FinCrime/AML experience, foundation model fine-tuning, LLM evaluation, establishing modern ML practices
Technologies
PyTorch, Python, Graph Neural Networks, Foundation Models
Responsibilities
Design and ship deep learning models for real-time fraud detection, define ML architecture strategy, build reusable end-to-end ML pipelines, evaluate foundation models for transaction representation, partner on model evaluation and causal measurement, mentor engineers on production best practices
Seniority
Staff, hands-on IC with strategic influence
