Interested in this AI/ML Engineer role at Charles Schwab?
Apply Now →About This Role
San Francisco, CA ; Chicago, IL ; Austin, TX
Requisition ID 2026\-124229 Category Legal \& Compliance Position type Regular Pay range USD $150,000\.00 \- $200,000\.00 / Year Application deadline 2026\-08\-04
Your opportunity
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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.
In Compliance, we help our business partners achieve their strategic priorities by promoting principles and practices that build client trust and safeguard the integrity of the firm.
The Director, Artificial Intelligence Compliance is a new role within Schwab Compliance and serves as a critical partner to specific businesses, peers in Compliance, other second line functions, Legal and Internal Audit. The Director, Artificial Intelligence Compliance leads an enterprise compliance risk management program that supports the company’s many key strategic initiatives to leverage artificial intelligence to enhance the client experience, grow the business, and operate more efficiently. This role engages through disparate regulated entities, and requires facility with multiple subject matter disciplines, including broker/dealer, investment adviser, banking, and others. As with all Schwab Compliance Director roles, the Director, Artificial Intelligence Compliance requires the successful leader to model and exhibit enterprise\-wide thinking and exceptional collaboration to support Schwab's business.
The Director, Artificial Intelligence Compliance helps shape the strategic direction of the companywide compliance risk management programs. Each Director, Schwab Compliance, shares the same measures of success:* Personal ownership of their assigned compliance risk management program and shared accountability for the successful execution of the Schwab compliance risk management program.
- Vivid day\-to\-day evidence of enterprise\-wide thinking, bringing the full Schwab Compliance perspective to every aspect of the job, including exceptional collaboration and boundaryless cooperation with peers on the Schwab Compliance Team demonstrating the lived values of \#OneSchwab and \#OneSchwabCompliance.
- Purpose driven leader who displays confidence and courage in business decisions, actions, and communications with a demonstrated ability to take decisive action when needed including championing ideas, positions, or dissent.
Enterprise\-wide thinking is an approach that considers the entire organization, rather than an individual business or entity. It involves the holistic view of regulatory impact and business strategies.
Collaboration, open communication, and alignment across teams are drivers for success. Enterprise\-wide thinking means taking a big picture view. Each Director, Schwab Compliance, engages with Senior and Executive Leaders at all levels across Schwab, and also is responsible for leading Schwab Compliance’s engagement with specific key stakeholders based on their assigned business support responsibilities.What you have
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Required Qualifications:* Minimum 10 years of related work experience in compliance, and risk management and anti\-money laundering; and a minimum of 5 years working with technology governance.
- Deep competencies in relevant financial services; securities laws and regulations; banking laws and regulations; consumer protection; anti\-money laundering and privacy.
- Embraces an enterprise view in identifying, assessing, mitigating, measuring, and monitoring Compliance risks consistent with the Enterprise Risk Management Framework.
- A degree is required, preferably in Business/Finance/Economics or related disciplines, post\-secondary degree, e.g., JD, MBA or equivalent, viewed favorably.
Preferred Qualifications:* Demonstrated success in transforming organizations int a highly collaborative environment to influence compliance and controls while building strong internal and external partnerships and providing guidance to varied stakeholders.
- Thrives on initiating, building, and cultivating strong authentic relationships inside and outside the group; highly collaborative; navigates and activates alignment across organizational silos.
- Relationship\-oriented executive who manages complex, multistakeholder issues with resilience, embraces others’ perspectives and understands the limits to their own points of view.
- Provides a clear, independent risk management perspective in accordance with the Board\-approved risk appetite.
- Proven ability to find balance in developing programs effectively meet potentially differing regulatory requirements and expectations while being workable for business partners.
- An exceptional communicator able to listen and influence across channels with a purpose. Confidence interacting with and influencing senior leaders across business lines and functions through innovative and creative means.
- Strong emotional intelligence and executive presence.
- Possess strong business drivers and perspectives combined with broad management and commercial skills, and an ability to bring a strategic focus to the function and be capable of adding value to the business.
- Anticipates the possible demands and outcomes of a particular task or situation – plans and prioritizes appropriately.
- Client focused; able to build relationships and establish trust, respect, competence, and confidence effectively and quickly.
What’s in it for you
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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
### Share:
- X
Eligible Schwabbies receive
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- Medical, dental and vision benefits
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- 401(k) and employee stock purchase plans
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- Tuition reimbursement to keep developing your career
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- Paid parental leave and adoption/family building benefits
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- Sabbatical leave available after five years of employment
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Salary Context
This $150K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Charles Schwab AI Hiring
Charles Schwab has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, Data Scientist. Positions span San Francisco, CA, US, Austin, TX, US, Southlake, TX, US. Compensation range: $139K - $250K.
Location Context
AI roles in San Francisco pay a median of $277,088 across 810 tracked positions. That's 27% above the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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