Interested in this AI/ML Engineer role at Charles Schwab?
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Austin, TX ; Westlake, TX Requisition ID 2026-127433 Category Data Analytics and Strategy Position type Regular Pay range USD $100,000.00 - $155,000.00 / Year Application deadline 2026-10-07
Your opportunity
At Schwab, you are empowered to make an impact on your career. Here, innovative thinking meets creative problem-solving as we challenge the status quo and transform the finance industry together.
Schwab's Corporate Strategy team serves as an internal strategy group and trusted advisor to executives and business leaders. The team provides strategic thought partnership, rigorous analysis, and actionable insights to support informed decision-making. Its work includes leading high-priority strategic initiatives, developing perspectives on industry trends and the competitive landscape, and supporting the annual corporate strategic planning cycle. The team partners with the Board, C-suite, and senior leaders on a broad range of complex, enterprise-wide priorities.
As Manager, AI Strategy, you will play a central role in shaping Schwab's enterprise AI strategy and connecting business priorities, AI capabilities, and execution. Working both independently and collaboratively, you will conduct data-driven analysis, synthesize complex information, and develop recommendations that help translate AI opportunities into sustainable business value.
This role offers the opportunity to work on some of Schwab's most important strategic priorities while building broad enterprise exposure and engaging with senior leaders across the organization.
What You Will Do
- Shape and advance Schwab's enterprise AI strategy, informed by a strong understanding of AI technologies, enterprise platforms, data, and business priorities.
- Develop clear, executive-ready materials and lead discussions with internal and external stakeholders, including senior leadership.
- Partner across business, data, technology, risk, and other functions to deliver high-quality work on time, maintain transparency, and build alignment.
- Synthesize large volumes of quantitative and qualitative information to identify insights, implications, and strategic opportunities.
- Create high-impact analyses, recommendations, and presentations that communicate complex ideas clearly and persuasively.
- Maintain an informed perspective on AI developments, industry trends, competitor activity, and other external factors that may influence Schwab's strategy.
- Support the review and communication of key elements of corporate strategy by collaborating with leadership and partners, including Communications, to promote clarity, alignment, and consistent messaging.
- Translate AI strategy into actionable roadmaps, solution concepts, and implementation plans in partnership with business, data, and technology leaders. Evaluate feasibility, dependencies, risks, and tradeoffs.
- Build trusted relationships across the firm and strengthen connections between Corporate Strategy and Schwab's functional organizations.
- Contribute to workstreams within large, complex initiatives while independently planning and executing smaller projects.
- Demonstrate professionalism, integrity, and sound judgment in all communications and in the handling of confidential strategic, financial, and other sensitive information.
What You Will Gain
- The opportunity to influence important AI strategy decisions that are shaping the future direction of the company.
- Experience balancing strategic theory with practical execution while accounting for industry trends, competitor actions, client needs, and real-world business considerations.
- Broader knowledge of Schwab's clients, businesses, and key value drivers, along with the opportunity to build credibility and trusted relationships with business and technology partners.
Workplace flexibility: This position follows a hybrid work model, Monday through Friday, with four days in the office and one day working from home. It is only available in Austin, TX and Westlake, TX (DFW). Candidate must reside in or be willing to relocate on their own to the listed locations.
Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.
What you have
To ensure that we fulfill our promise of “challenging the status quo,” this role has specific qualifications that successful candidates should have.
Required Qualifications:
- 4 or more years of experience in management consulting, corporate strategy, business strategy, or a related strategic planning function.
- Bachelor's degree.
- Demonstrated experience in financial modeling, strategic planning, market research, competitive analysis, or business development.
- Strong problem-solving, critical thinking, and storytelling capabilities, with proficiency in Microsoft Excel and PowerPoint.
- Excellent written and verbal communication skills, including experience developing executive-ready materials and presenting complex concepts to diverse audiences.
- Strong analytical skills, sound judgment, and close attention to detail.
- Demonstrated ability to build trusted relationships, collaborate across functions, and influence outcomes through partnership and stakeholder engagement.
- Intellectual curiosity and a passion for learning, with the ability to manage multiple priorities and subject areas simultaneously.
- Ability to navigate ambiguity, adapt to evolving priorities, and perform effectively in a dynamic, fast-paced environment.
Preferred Qualifications:
- Interest in AI technology and its potential to create value across financial services and Schwab's lines of business.
- Technical fluency in AI, data, enterprise technology, or a related field.
- Experience in the financial services industry.
- MBA or another relevant advanced degree.
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 $100K-$155K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($127K) sits 40% below the category median. Disclosed range: $100K to $155K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Charles Schwab AI Hiring
Charles Schwab has 5 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span San Francisco, CA, Southlake, TX, US, Austin, TX, US. Compensation range: $155K - $160K.
Location Context
AI roles in Austin pay a median of $207,800 across 271 tracked positions. That's 3% below 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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