ML Engineer — fraud & GenAI abuse detection

Great Lakes Risk ML detects synthetic identities, authorised push payment scams, and GenAI-assisted fraud across North American FI partners. Precision and fairness matter as models touch customer decisions.Hiring an ML Engineer in Chicago (hybrid) to deepen graph-and-text ensembles and accelerate investigations tooling.What you will doPrototype and productionise models combining transactional graphs, behavioural sequences, and document signals.Partner with fairness analysts on disparate impact monitoring and corrective actions.Build investigative UI affordances linking model explanations to SAR workflows.Integrate experimentation with AML/fraud ops for feedback loops.Harden deployments for SOC2-aligned logging and segregation of duties.What we look for4+ years ML engineering with adversarial problem spaces (fraud, trust, integrity).Strong Python, SQL, PyTorch; experience with Snowflake-scale analytics.Ability to articulate model limitations to auditors and regulators.US work authorisation.Nice to haveGraph neural networks or temporal GNN tooling in production.Experience with alert triage UX for analyst teams.