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Westlake, TX ; Austin, TX ; Orlando, FL Requisition ID 2026-127783 Category Project Management, Product Management Position type Regular Pay range USD $120,000.00 - $185,000.00 / Year Application deadline 2026-10-14
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. We believe in the importance of in-office collaboration and fully intend for the selected candidate for this role to work on site in the specified location(s). Applicants must be currently authorized to work in the United States on a full-time basis without employer sponsorship.
The Transformation Management Office (TMO) at Schwab defines, leads, and accelerates large, cross-enterprise transformations that drive meaningful business and client outcomes. As Senior Manager, Product AI Enablement (individual contributor), you will help shape how Product leverages AI, by turning emerging capabilities into practical workflows, tools, and standards that enable teams to move faster, make better decisions, and deliver measurable value. You will own a workstream for the enterprise effort to evolve Product ways of working across product and software development lifecycles, including AI-enabled and AI-driven product development lifecycles (AIDLC). Working with cross-functional partners, you will identify high-value opportunities, clarify Product responsibilities and decision rights, and translate AI capabilities into repeatable practices from opportunity framing and discovery through planning, development, validation, launch, and measurement. You will build, test, and scale Product standards, playbooks, role guidance, capability expectations, and reusable assets that support responsible AIDLC adoption across different product areas and levels of maturity. You will partner with Product, Engineering, UX, Data, Architecture, Risk, and Business leaders to embed these practices into planning, delivery, governance, tooling, talent, and learning programs without creating unnecessary process. Through pilots, prototypes, practitioner feedback, and telemetry, you will test assumptions, evaluate adoption, identify friction, and refine what works. You will help teams determine where AI can accelerate Product workflows, where human judgment and accountability remain essential, and how workflow changes translate into measurable improvements in quality, speed, capacity, client experience, and business outcomes. Success in this role means Product teams can confidently AI-enabled practices, use practical tools and guidance, and remain accountable for strategy, outcomes, quality, risk, and client value. The solutions you help establish will scale across the enterprise, evolve as AI capabilities mature, and strengthen how Product and Technology teams build, learn, and deliver together. Key Responsibilities
- Own and lead a defined AIDLC workstream, identifying high-value opportunities and establishing clear Product responsibilities, decision rights, quality expectations, and cross-functional collaboration points across the end-to-end lifecycle.
- Build, test, and scale Product standards, playbooks, templates, maturity guidance, and reusable assets that strengthen problem framing, requirements, prioritization, trade-off decisions, risk management, and outcome measurement while allowing appropriate flexibility across products and delivery models.
- Partner with Product, Technology, UX, Data, Architecture, Risk, Business, and talent leaders to integrate AIDLC standards into operating models, governance, planning, delivery practices, tooling, performance measures, role expectations, and learning programs.
- Own a defined component of the roadmap for AI-driven tools, platforms, agents, and skills that support Product workflows, translating practitioner needs into requirements and prioritizing capabilities based on user value, strategic alignment, feasibility, risk, and expected business impact.
- Clarify how AI tools and agents reshape Product workflows, defining where automation can accelerate work, where human judgment and accountability remain essential, and what responsible-use controls, validation, skills, and adoption support are required.
- Lead pilots, prototypes, and continuous learning cycles that test assumptions, evaluate adoption, and measure improvements in quality, speed, capacity, client experience, and business outcomes; translate evidence and practitioner feedback into roadmap decisions and scalable improvements.
What you have
Required Qualifications:
- Bachelor’s degree; quivalent relevant experience will also be considered
- 5+ years of experience across product management, product operations, product enablement, or transformation, with strong end-to-end Product expertise spanning discovery, prioritization, requirements, delivery, launch, and outcome measurement
2+ years of experience * ing AI-enabled or AI-driven approaches to product development, including identifying high-value use cases, evaluating emerging capabilities, and translating them into practical Product workflows
- Working knowledge of context engineering and AI-ready product documentation, including approaches for research and information synthesis, enterprise knowledge retrieval, memory systems, context libraries, and business capability maps
- Experience evolving traditional user stories into structured, reusable specifications, such as context packs or specification kits, that give AI tools and agents the goals, business rules, source knowledge, constraints, evaluation criteria, and acceptance criteria needed to produce reliable outcomes
- Experience using pilots, practitioner feedback, product and operational data, and measurable outcomes to prioritize opportunities, test assumptions, evaluate adoption and business impact, and continuously improve solutions
Preferred Qualifications:
- Advanced degree in engineering, technology, business or a related field
- High emotional intelligence and strong facilitation, communication, and influence skills, with demonstrated ability to listen actively, build trust, navigate conflict constructively, adapt to diverse perspectives, and align stakeholders in a complex matrixed environment
In addition to the salary range, this role is also eligible for bonus or incentive opportunities.
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-$185K 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 ($152K) sits 28% below the category median. Disclosed range: $120K to $185K.
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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