Senior Specialist, AI Model Validation

Charles Schwab; Austin (TX); Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

401(k) with company match

Employee stock purchase plan

Paid vacation

Sabbatical after 5 years

Parental leave and family benefits

Tuition reimbursement

Health, dental, and vision insurance

Job summary

Charles Schwab, part of Model Risk Oversight, invites early-career engineers and quantitative researchers to validate AI/ML models, with a focus on generative AI and LLM-based applications. You will design tests, analyze model behavior, and present evidence-based conclusions to technical teams and senior leaders.

The role offers a hybrid schedule (4 days in office, 1 day remote), with opportunities to build reusable validation frameworks and collaborate across risk partners and engineers.

Qualifications

• Master’s or Bachelor’s with strong relevant project or research experience.
• 0–2 years of professional experience; internships or research count.
• Proficiency in Python and data science libraries.
• Working knowledge of ML concepts, experimental design, and evaluation.
• Ability to read technical documentation and communicate conclusions clearly.
• Curiosity, judgement, attention to detail, and evidence-based thinking.

Responsibilities

• Validate AI/ML models across use cases, data, design, and controls.
• Build reproducible testing frameworks and analyses for quality and safety.
• Evaluate generative AI and retrieval-generated prompts and grounding.
• Apply statistical/ML methods to benchmark models and analyze errors.
• Review code, notebooks, and production monitoring evidence.
• Collaborate with developers, product, data scientists, and risk partners.
• Document findings in validation reports and executive summaries.
• Participate in ongoing performance monitoring and reviews.
• Contribute to reusable validation frameworks and testing tools.

Skills

Python

Statistical reasoning

Experiment design

Data analysis

Technical communication

Education

Bachelor's degree

Master's degree

Tools

SQL

Git

Containers

Cloud platforms

Job description

• Understanding software testing, data quality, reproducibility, or model deployment concepts.

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 behaviour, 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 behaviour, retrieval quality, groundedness, hallucination risk, response consistency, and guardrail effectiveness.
• The candidate will apply statistical and machine learning methods to benchmark models, analyse 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 judgement, 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