Senior Model Risk Management Specialist (AI/ML)

TL;DR

Senior Model Risk Management Specialist (AI/ML): Challenging and validating high-stakes ML models for lending, fraud, and AML with an accent on conceptual soundness, benchmarking, and stress testing. Focus on building agentic validation tooling for GenAI systems and ensuring model reliability under shifting populations.

Location: Remote within approved US locations

Salary: $194,500 — $343,100 USD

Company

hirify.global builds simple, powerful tools for an open economy, including brands like Square, Cash App, Afterpay, and TIDAL.

What you will do

• Independently challenge model owners across lending, fraud, and AML to ensure model soundness.
• Hunt and prove silent errors in metrics and identify issues with population drift before production.
• Develop evaluation strategies for rare events and shifting populations that may appear after launch.
• Build and orchestrate agentic validation tooling for LLM-powered systems.
• Evaluate ML systems end-to-end, analyzing the fit between features, training, serving, and monitoring.
• Translate explainability and fair-lending findings into model and product decisions.

Requirements

• Quantitative degree and senior-level experience building or validating models in high-stakes domains (credit, fraud, or financial crime).
• Expertise in effective-challenge methodology, including reproduction, benchmarking, and stress testing.
• Deep applied knowledge of ML families (regression, tree ensembles, deep learning) and statistical rigor.
• Proficiency in production-quality Python and SQL for handling large datasets.
• Fluency with modern AI, specifically building with LLMs and agentic tools.
• Must be based in approved US locations.

Nice to have

• Familiarity with model risk management frameworks and fair-lending standards.

Culture & Benefits

• Remote-friendly work environment.
• Medical insurance and retirement savings plans.
• Flexible time off and modern family planning support.
• Inclusive workplace with a commitment to fair and square hiring practices.