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About This Role
Westlake, TX
Requisition ID 2026\-123740 Category Marketing \& Communications Position type Regular Pay range USD $120,000\.00 \- $160,000\.00 / Year Application deadline 2026\-07\-17
Your opportunity
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At Schwab, you’re empowered to make an impact on your career. Here, innovative thinking meets creative problem solving, challenging the status quo, and transforming the financial services industry.
As part of the Advisor Services (AS) Marketing Organization, the Advisor Services Digital Experience Team is responsible for the digital strategy, optimization, and ongoing management of advisorservices.schwab.com across both public and secure (post\-login) experiences.
We’re seeking a senior digital marketing leader to help build, scale, and support the Advisor Services' modern search and AI visibility capability, including SEO, AI visibility (AEO), and performance optimization. Working closely with the Director of AS Digital Experience, this role will help establish the roadmap, governance model, measurement framework, and optimization approach that will guide how Advisor Services is discovered, consumed, and engaged with across digital channel.
This digital marketing role will play an important part in Advisor Services' evolution to a modern digital ecosystem, including migration to a headless CMS, global taxonomy, structured content models, reusable templates, and AI\-ready experiences.
Success will require balancing traditional digital marketing expertise with emerging AI visibility best practices and is instrumental in advancing Advisor Services' search and AI visibility capabilities, helping establish repeatable approaches for content optimization, measurement, and ongoing performance improvement.
This role combines the mindset of a digital marketer, the curiosity of an optimizer, and the versatility of a Marketing Generalist, balancing web, content, search, AI visibility, and performance optimization expertise while staying adaptable as priorities evolve.What You’ll Do
Lead SEO \& AI Visibility Execution* Alongside AS Digital Experience Team Director, lead the execution of SEO and AI visibility (AEO) strategies aligned with enterprise frameworks and goals.
- Ensure content and experiences are optimized for:
- + Search discoverability
+ AI answer inclusion
+ Credibility and authority signals
- Translate strategy into clear, actionable requirements across content, design, and platform teams.
- Identify content opportunities and develop recommendations that improve search visibility, AI discoverability, engagement, and business outcomes. Partner with content strategists and experience owners to prioritize and implement improvements.
- Own ongoing SEO/AEO performance tracking and optimization
- Manage external SEO/AEO vendor relationships/agencies, including execution and performance outcomes.
- Partner with enterprise SEO/AEO stakeholders to ensure alignment with broader Schwab initiatives.
- Lead coordination and governance activities across Advisor Services Marketing and enterprise partners to ensure alignment on Advisor Services SEO, AI visibility, and optimization priorities.
- + Define standards, best practices, and prioritization frameworks.
+ Ensure SEO and AI requirements are embedded into roadmaps and planning efforts.
+ Align stakeholders around shared goals, opportunities, and measurement approaches.
+ Serve as a key connector between Advisor Services and enterprise teams advancing AI visibility initiatives.
- Define and evolve standards, best practices, and prioritization frameworks.
- Ensure SEO and AI requirements are proactively built into roadmaps.
- Align priorities and drive clarity across teams.
- Function as a key connector between Advisor Services and enterprise site partners and partner with content developers, PR, social, and enterprise teams to support a coordinated approach to search and AI visibility.
Own and Lead Performance Optimization* Develop and own the sitewide optimization roadmap and experimentation program.
- Build and lead a strong test\-and\-learn approach, including:
- + A/B and multivariate testing
+ Usability testing and advisor research
- Identify and prioritize opportunities to improve:
- + Engagement
+ Task completion
+ Conversion outcomes
- Partner with teams to implement and scale improvements.
- Contribute to personalization strategy and testing where appropriate.
- Collaborate closely with the team’s Web Report manager and analytics team and turn insights and learnings into clear, actionable optimization plans.
- Define success metrics and performance measures for SEO, AI visibility, and optimization efforts.
- Continuously assess what’s working, where gaps exist, what to optimize next
Partner across public and secure experiences:* Work across both public and post\-login environments.
- Ensure consistency in:
- + Onsite search strategies
+ Optimization approaches
- Partner with experience owners to align improvements while supporting each channel’s unique needs.
Operate as a strategic marketing generalist. This role is intentionally designed with flexibility. You will step into high\-priority initiatives to support and execute marketing initiatives when:* + Bandwidth is constrained.
+ Speed is critical.
+ Business impact is high.
- Contribute across:
- + Experience launches.
+ Content development, copywriting support, creative development, and project management.
+ Content and page\-level optimization
+ Rapid test\-and\-learn cycles.
- Pivot quickly as priorities evolve.
A Day in Your Life:* Managing an integrated SEO, AI visibility, and optimization roadmap
- Partnering across teams to align priorities and execution
- Reviewing performance data and identifying opportunities
- Leading test design and experimentation planning
- Helping shape AI\-ready content recommendations and optimization opportunities
- Help establish AI\-ready content best practices and optimization approaches for Advisor Services
- Managing agency/vendor relationships
- Stepping in and pivoting to support initiatives, campaigns, and areas where another set of hands is needed.
- Collaborating with colleagues in\-office and across teams
You’re Really Good At* Thinking like a modern digital marketer, connecting marketing principles, SEO, AI, UX, and performance
- Understanding customer journeys and translating audience needs into content and experience improvements
- Developing content and digital experiences that support business and marketing objectives.
- Balancing data, creativity, content strategy, and user experience
- Leading test\-and\-learn approaches and experimentation programs.
- Translating insights into clear, actionable outcomes
- Working, influencing, and aligning across a matrixed organization
- Managing agency/vendor relationships to deliver measurable results
- Operating as both a specialist and generalist, shifting between strategy and execution
- Working in ambiguity and pivoting quickly as priorities change
- Operating in a highly collaborative environment and building strong partnerships both in person and virtually.
NOTE: This role is based in the office and requires a minimum of four days per week on site.What you have
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- Experience supporting and optimizing large public\-facing brand websites.
- Familiarity with content management systems, taxonomy, information architecture, and website governance
- Working knowledge of UX principles and digital customer experiences
- Experience partnering with design, content, analytics, and technology teams to improve digital experiences.
- 5\+ years of experience in:
- + SEO programs and familiarity with emerging AI visibility approaches (AEO/GEO)
+ Digital marketing, including digital content development and marketing brief development.
+ Optimization/experimentation
+ Website performance and analytics
- Strong knowledge of:
- + SEO (technical and content)
+ AI visibility / AEO (or emerging expertise)
+ Conversion optimization and testing frameworks
- Experience with:
- + Analytics platforms (e.g., Adobe Analytics or similar)
+ Firsthand experience and usage of SEO/AEO tools (e.g., SEMrush, Profound, Adobe Analytics or similar)
+ Experimentation platforms (e.g., Optimizely or similar)
+ Managing agency/vendor partners.
+ Partnering across enterprise and cross\-functional teams.
+ Proven ability to lead and manage multiple priorities.
+ Well\-rounded digital marketing experience across web and content
+ Experience creating, editing, optimizing, or briefing content for digital channels.
+ Effective communication skills, both written and verbal
+ Strong proficiency in PowerPoint, Excel, and MS Office tools
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 $120K-$160K 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 Required
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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($140K) sits 36% below the category median. Disclosed range: $120K to $160K.
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
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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.
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