Senior Specialist, AI Model Validation

Charles Schwab; Southlake (TX); Hybrid; USD 110,000 - 140,000; Full time

Benefits offered by this job

401(k) with company match

Sabbatical after 5 years

Paid parental leave

Tuition reimbursement

Health, dental, and vision insurance

Job summary

Charles Schwab is seeking an early‑career engineer or quantitative researcher to join the AI Model Validation team in a hybrid role (4 days in-office, 1 day remote) in Texas. You will validate AI/ML models, build testing frameworks, and present evidence-based conclusions to technical teams.

You'll work on generative AI, LLMs, and model governance with collaboration across developers, data scientists, risk teams, and leadership.

Qualifications

• Master's degree or Bachelor's with strong project/research experience in CS, data science, statistics, or related field.
• 0–2 years of professional experience; internships/projects welcome.
• Proficiency in Python and common data science libraries.
• Ability to read technical docs, investigate unfamiliar systems, and communicate conclusions.

Responsibilities

• Validate AI/ML models across use cases, focusing on generative AI and LLMs.
• Build reproducible testing frameworks and analyses to assess quality, safety, and reliability.
• Evaluate prompt behavior, retrieval quality, and guardrail effectiveness in AI systems.
• Apply ML methods to benchmark models, analyze errors, and compare options.

Skills

Python

ML engineering

Data analysis

Experiment design

Communication

Education

Master's degree / Bachelor's with relevant project experience

Tools

SQL

Git

Containers

Cloud platforms

Job description

Your opportunity

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.

What you have

Required Qualifications

• Master's degree, or bachelor's degree with strong relevant project or research experience, in computer science, data science, statistics, applied mathematics, engineering, economics, quantitative finance, or a related field.
• 0-2 years of professional experience. Relevant internships, research, capstone projects, open-source contributions, or substantial independent projects are welcome.
• Proficiency in Python and experience using common data science or machine learning libraries.
• Working knowledge of core machine learning concepts, experimental design, model evaluation, and statistical reasoning.
• Ability to read technical documentation and code, investigate unfamiliar systems, and communicate conclusions clearly.
• Curiosity, sound judgment, attention to detail, and a willingness to challenge assumptions with evidence.

Preferred Qualifications

• Hands-on experience with large language models, generative AI, natural language processing, embeddings, vector search, or retrieval-augmented generation.
• Experience with evaluation frameworks, prompt testing, red teaming, model monitoring, or responsible AI techniques.
• Familiarity with SQL, Git, containers, cloud platforms, or modern ML development workflows.
• Understanding software testing, data quality, reproducibility, or model deployment concepts.
• Experience presenting technical work through research papers, project reports, demos, or presentations.
• Interest in financial services, model risk management, AI governance, or building trustworthy AI systems.

How you will succeed

You do not need to arrive as an expert in financial regulation or model validation. Successful candidates bring strong technical fundamentals, intellectual curiosity, disciplined problem solving, and the ability to explain complex work clearly. We value thoughtful individuals who enjoy learning quickly, testing ideas, finding weaknesses before they become problems, and improving evaluation of AI systems.

Role details

• Career level: Senior Specialist, Risk Analytics/Modeling
• Team: Model Risk Oversight, Artificial Intelligence Validation
• Role type: Individual contributor
• Primary focus: AI/ML and generative AI model validation, effective challenge, and ongoing model oversight

What’s in it for you

At Schwab, you’re empowered to shape your future. We champion your growth through meaningful work, continuous learning, and a culture of trust and collaboration—so you can build the skills to make a lasting impact. Our Hybrid Work and Flexibility approach balances our ongoing commitment to workplace flexibility, serving our clients, and our strong belief in the value of being together in person on a regular basis.

We offer a competitive benefits package that takes care of the whole you – both today and in the future:

• 401(k) with company match and Employee stock purchase plan
• Paid time for vacation, volunteering, and 28-day sabbatical after every 5 years of service for eligible positions
• Paid parental leave and family building benefits
• Tuition reimbursement
• Health, dental, and vision insurance