Interested in this AI/ML Engineer role at Charles Schwab?
Apply Now →About This Role
Westlake, TX ; Austin, TX
Requisition ID 2026\-123068 Category Data Analytics and Strategy Position type Regular Pay range USD $197,800\.00 \- $247,400\.00 / Year Application deadline 2026\-06\-26
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.We believe in the importance of in\-office collaboration and fully intend for the selected candidate for this role to work on site 4 days per week in one of the specified locations.Applicants must be currently authorized to work in the United States on a full\-time basis without employer sponsorship.
Reporting to the Head of Workplace Services Experience, this leader will define and advance AI strategy across client\-facing capabilities and internal tools to enhance client experience. This leader will focus on AI adoption for Workplace Services (WS) by translating enterprise and WS strategy into practical, scalable AI capabilities that enable responsible employee use, support client AI needs, and drive business valueKey Outcomes* Use AI to Strengthen Client relationships: Deploy AI to provide proactive insights, predictive client health monitoring, and enhanced service experiences that deepen trust and reduce attrition.
Personalize Participant Experiences: In partnership with the enterprise* AI to deliver highly tailored, data\-driven participant journeys that increase engagement, improve outcomes, and drive conversion into broader Schwab solutions.
- Improve our Ability to Sell: Leverage AI to enhance sales effectiveness through intelligent targeting, automated content generation, and data\-driven insights that increase win rates and accelerate deal cycles.
- Optimize Processes: Eliminate manual work, reduce errors, and accelerate processing by embedding AI in core operations.
- Empower Employees: Equip employees with AI copilots and automation tools that enhance productivity, elevate decision\-making, and enable more impactful client and participant interactions.
Key ResponsibilitiesAI strategy and program leadership* Lead AI strategy and execution across Workplace Services, aligning with business objectives and client experience goals.
- Define and execute the WS AI vision, strategy, and multi\-year roadmap aligned to business and client priorities.
- Align with enterprise AI frameworks and partner across Digital, Technology, Risk, Legal, Compliance, Security, HR, and Communications to ensure responsible deployment.
- Serve as WS point of contact for AI related items for Corporate or other Schwab enterprises.
- Define and track success metrics (adoption, usage, impact, risk, and business value).
- Provide executive reporting, recommendations, and governance via leadership and steering committees.
- Partner with WS Risk to build AI governance, standards, and risk practices for AI internal and client facing capabilities with enterprise leaders.
- Influence senior stakeholders across business, technology, data, risk, legal, and compliance to drive alignment and execution.
- There is potential to build and lead a team required to support AI adoption, governance, and ongoing enablement.
AI value realization* Own the WS AI operating model, including governance, decision rights, intake, prioritization, and cross\-functional coordination.
- Identify and scale high\-value AI use cases that improve productivity, service quality, employee experience, and operational efficiency.
- Establish a structured approach to evaluate and respond to client AI requests, partnering with the enterprise and Marketing on client facing materials and RFP answers.
Adoption, enablement, and change management* Build and manage the AI program, including MVP delivery, roadmap execution, and budget oversight.
- Lead communication, stakeholder engagement, training, and reinforcement.
- Build organizational AI fluency through role\-based learning, playbooks, and self\-service resources.
- Create feedback loops to monitor adoption, identify barriers, and continuously improve tools and processes.
- Stand up and lead a network of AI champions or ambassadors to accelerate adoption, surface use cases, and share best practices across teams.
What you have
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To ensure that we fulfill our promise of "challenging the status quo," this role has specific qualifications that successful candidates should have.Required Qualifications:* Bachelor’s degree.
- Leadership experience in financial services, fintech, or related industry, with strong knowledge of Defined Contribution and Equity Compensation services, product ecosystems, and client service models.
- Experience developing and scaling AI, data, digital, or platform strategies in complex, highly regulated environments.
- Track record of leading enterprise initiatives across business, product/offer, digital, data, risk, legal, and compliance organizations.
Preferred Qualifications:* Advanced degree.
In addition to the salary range, this role is also eligible for bonus or incentive opportunities.
\#workplacejobsWhat’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 $197K-$247K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($222K) sits 20% above the category median. Disclosed range: $197K to $247K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Charles Schwab AI Hiring
Charles Schwab has 6 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer, Data Scientist. Positions span Austin, TX, US, Westlake, TX, US, Southlake, TX, US. Compensation range: $94K - $247K.
Location Context
Across all AI roles, 14% (583 positions) offer remote work, while 3,532 require on-site attendance. Top AI hiring metros: New York (2,760 roles, $211,000 median); San Francisco (2,258 roles, $253,000 median); Los Angeles (1,841 roles, $195,000 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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