Senior Machine Learning Engineer, Model Risk Management

You will independently challenge model owners across lending, fraud, and AML; reproduce their results, set and defend the acceptance thresholds, and own the call on whether a model is sound. Hunt silent errors that make metrics lie, evaluate models under real-world conditions, ship production validation tooling, build agentic validation systems, assess ML systems end to end, connect explainability and fair-lending findings to product decisions, and help define standards for validating production AI.

Responsibilities

• Independently challenge model owners across lending, fraud, and AML; reproduce results, set acceptance thresholds, and decide whether models are sound.
• Identify silent errors that mislead metrics and prove them out before production.
• Design evaluation for rare events, shifting populations, and post-launch drift.
• Work hands-on in unfamiliar codebases and ship production validation tooling.
• Build agentic validation tooling that orchestrates parallel agents.
• Evaluate ML systems end to end across features, training, serving, monitoring, and scale.
• Connect explainability and fair-lending findings on consumer credit models to model and product decisions.
• Help define standards for validating production AI systems.

Requirements

• Quantitative degree or equivalent experience and senior individual-contributor depth building or validating models in credit, fraud, or financial crime.
• Experience with effective challenge methodology, reproduction, conceptual soundness review, benchmarking, stress testing, and outcomes analysis.
• Deep applied machine learning and statistics across regression, tree ensembles, and deep learning.
• Strong experimentation and statistical rigor, including holdout design, uncertainty, calibration, and generalization.
• Production-quality Python, SQL on large datasets, reproducible code, and testing practices.
• Fluency building with LLMs and agentic tools, with judgment about trustworthy outputs.
• Familiarity with model risk management frameworks and fair-lending standards.
• Strong communication skills and independence operating under ambiguity.

Benefits

• Remote work
• Medical insurance
• Flexible time off
• Retirement savings plans
• Modern family planning