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Westlake, TX ; Austin, TX Requisition ID 2026-127508 Category Risk & Regulatory Position type Regular Pay range USD $120,000.00 - $170,000.00 / Year Application deadline 2026-10-16
Your opportunity
At Schwab, you’re empowered to make an impact on your career. Here, innovative thought meets creative problem solving, helping us “challenge the status quo” and transform the finance industry together. 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. The Senior Manager of Retail Supervision & Risk Management will serve as the primary 1st Line of Defense (1LOD) control point for determining regulatory supervision requirements for enterprise-wide and retail-facing models (including AI/ML and rules-based systems). This role is responsible for ensuring that model-driven activities are aligned with regulatory expectations and are supported by effective supervisory controls, guardrails, evaluations and policies. This role will serve in as a key subject matter expert (SME) and will partner across Compliance, Risk, Technology, Product, and others to evaluate model use cases, identify regulatory risks, and implement practical, scalable control frameworks that protect clients and uphold firm obligations under regulations such as but not limited to Regulation Best Interest (Reg BI), DOL fiduciary standards, Communications with the Public, and Money Movement controls. Key Responsibilities 1LOD Model Supervision & Risk Leadership
- Serve as the central supervisory authority for retail and enterprise models impacting internal and external interactions, recommendations, and account activity.
- + Assess and approve/deny models prior to planned deployment
- Evaluate AI and analytical models to identify regulatory, client, and business operational risks.
- + Participate in working groups as the supervision SME, where applicable.
- Partner with Product teams to (not all inclusive):
- + Develop/refine supervisory guardrails and evaluations for model-enabled workflows + Determine and align on thresholds, severity methodology, and escalation protocols
- Develop and maintain a model supervision framework, including but not limited to:
- + Risk tiering and model classification + Guardrail standards (e.g., hard limits vs. advisory prompts) + Human-in-the-loop intervention requirements
- Define clear ownership and accountability structures across first-line and second-line partners.
- Assess and provide modification recommendations, where applicable, to Model Whitepapers, Workflows, and other applicable artifacts.
1LOD Supervisory Control Design
- Translate regulatory requirements into operationally practical, scalable, testable controls.
- Design and implement preventive, detective, triage, and directive controls aligned to model risks.
- Partner with Retail Business Unit Supervision SMEs to construct supervisory controls, where applicable.
- Partner with Compliance to ensure audit and exam readiness, including documentation, evidence, and rationales for supervisory approaches.
Monitoring, Testing & Continuous Improvement
- Establish ongoing monitoring and surveillance strategies for model outputs and outcomes.
- Calibrate on testing frameworks (scenario testing, adverse outcome testing, where applicable) with Product and Compliance partners.
- Use data insights to better partner with Product and Compliance teams to refine controls, adjust guardrails, and mitigate emerging risks.
Training & Policy Enablement
- Partner with SMEs to develop targeted training programs for supervisors and frontline teams on AI usage, risks, and personnel responsibilities.
- Responsible for the development of policies and WSPs to reflect evolving AI-enabled supervision practices.
Core Skills & Capabilities
- Regulatory translation: Ability to convert complex regulations into actionable controls and guardrails
- Risk-based thinking: Strong judgment in prioritizing high-impact risks and ing proportionate controls
- Model literacy: Understanding of AI/ML concepts, limitations, and risk implications
- Supervisory design: Expertise in building scalable, defensible supervision frameworks
- Influence & collaboration: Ability to drive alignment across diverse stakeholders
- Communication: Executive-ready storytelling and clear documentation of risk decisions
What you have
Required Qualifications:
- 8–12+ years in broker-dealer and/or investment advisor supervision, risk management, or compliance, with a strong retail focus.
- Demonstrated experience with Reg BI, DOL fiduciary standards, communications supervision, and/or money movement controls.
- Hands-on involvement in AI/ML model risk management or model governance.
- Experience designing and implementing supervisory controls in a regulated financial services environment.
- FINRA Series 7, 66, and 9/10 (or equivalent).
Preferred Qualifications:
- Direct experience overseeing AI-enabled or automated recommendation systems.
- Familiarity with model validation, explainability, and governance considerations.
- Experience with regulatory exams, internal audits, and remediation programs related to model or supervisory risk.
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
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Eligible Schwabbies receive
- Medical, dental and vision benefits
- 401(k) and employee stock purchase plans
- Tuition reimbursement to keep developing your career
- Paid parental leave and adoption/family building benefits
- Sabbatical leave available after five years of employment
Salary Context
This $120K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Charles Schwab, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($145K) sits 32% below the category median. Disclosed range: $120K to $170K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Charles Schwab AI Hiring
Charles Schwab has 6 open AI roles right now. They're hiring across AI/ML Engineer, AI Safety, AI Software Engineer. Positions span Westlake, TX, US, San Francisco, CA, San Francisco, CA, US. Compensation range: $170K - $234K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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