ML Engineer

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 do

• Prototype 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 for

• 4+ 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 have

• Graph neural networks or temporal GNN tooling in production.
• Experience with alert triage UX for analyst teams.