Senior Specialist, AI Model Validation

At Schwab, innovative thinking meets practical problem solving. As part of Model Risk Oversight, you will work at the intersection of AI /ML engineering, quantitative analysis, and responsible model governance. You will help evaluate AI and machine learning systems before and after they reach production, with a particular focus on generative AI and applications that use large language models.

The AI Model Validation Team designed this role for an early-career engineer or quantitative researcher who wants hands-on exposure to real AI systems, rigorous experimentation, and high-impact technical communication. You will write code, design tests, analyze model behavior, investigate failure modes, and present evidence-based conclusions to technical teams and senior leaders.

Please note: This position is M-F during standard business hours with a hybrid work model (4 days in-office, 1 day working from home). It is only available in the areas listed. Candidate must reside or be willing to relocate on their own to one of the listed areas. Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.

What you will do

• Validate AI and machine learning models by independently assessing intended use, design choices, data, implementation, controls, performance, and limitations.
• Build reproducible testing frameworks, analyses, and prototypes to evaluate model quality, robustness, safety, and reliability.
• Evaluate generative AI and retrieval-augmented generation applications, including prompt behavior, retrieval quality, groundedness, hallucination risk, response consistency, and guardrail effectiveness.
• The candidate will apply statistical and machine learning methods to benchmark models, analyze errors, compare alternatives, and assess results.
• Review source code, notebooks, technical documentation, model inventories, performance monitoring, and production-control evidence.
• Partner with model developers, product teams, data scientists, engineers, risk partners, and external consultants to understand systems and challenge assumptions constructively.
• Document findings in clear validation reports, technical appendices, issue statements, and executive-ready presentations.
• Evaluate ongoing performance monitoring and participate in periodic reviews of models after implementation.
• Contribute to reusable validation frameworks, testing utilities, and effective-challenge methods as AI capabilities and industry practices evolve.

 What you will learn

• How to design production AI systems, testing, governance, and monitoring in a large financial institution.
• How to translate open-ended model risk questions into structured experiments and defensible conclusions.
• How to communicate technical findings to audiences ranging from ML engineers to senior management.
• How responsible AI, model risk management, software controls, and regulatory expectations come together in practice.