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About This Role
Austin, TX
Requisition ID 2026\-123949 Category Engineering \& Software Development Position type Regular Pay range USD $150,000\.00 \- $185,000\.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. 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).
Schwab is seeking a Senior Software Engineer to join Enterprise Architecture Solutions Engineering within Enterprise Architecture and the Office of the Chief Technology Officer (CTO). In this hands\-on engineering role, you’ll help build and evolve the internal platforms, reusable frameworks, AI tooling, golden paths, agentic infrastructure, and governance guardrails that enable Schwab engineering teams to scale AI responsibly and effectively. You’ll contribute directly to platforms including technical debt management, enterprise governance, agent registries, agent pipelines, architecture assessment, and internal AI enablement solutions, using strong engineering judgment to design systems that are scalable, secure, observable, and production\-ready.
This role is ideal for an engineer who enjoys working across technologies, ing AI and large language models as force multipliers, and moving between platforms, frameworks, and product priorities as enterprise needs evolve. Your impact will come through the systems you ship, the standards you model, and the way you partner with architects, engineers, and stakeholders to solve complex problems with clarity, precision, and accountability.What you have
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Required Qualifications* Bachelor’s or Master’s degree in Computer Science or equivalent professional experience.
- Demonstrated senior\-level engineering depth through hands\-on ownership of full\-stack systems shipped end to end.
- Proven experience designing and delivering AI systems in production environments.
- Depth in at least two of the following: agentic systems, tool orchestration, MCP or related protocols, retrieval\-augmented generation, embeddings, vector search, knowledge retrieval pipelines, prompt/context engineering, formal AI evaluation, testing, and guardrails.
- Strong command of enterprise design patterns, domain\-driven design, distributed systems, service interfaces, and production\-ready architecture.
- Ability to decompose complex problems, define precise technical specifications, direct AI\-assisted development effectively, and critically evaluate model\-generated output.
- Strong working knowledge of cloud platforms with the ability to reason about scalability, resilience, cost, security, and production operations for AI workloads.
- Hands\-on database expertise across relational, NoSQL, vector, or graph databases, including data modeling, query tuning, indexing, query plans, and storage trade\-off decisions.
- Experience reviewing code, pairing with engineers, improving development practices, and setting engineering standards through shipped reference implementations.
- Strong communication, problem\-solving, and collaboration skills with the ability to partner across business, technology, architecture, and engineering audiences.
- Commitment to responsible AI practices, including security, privacy, evaluation, governance, and safety built into delivery.
Preferred Qualifications* Experience working in private\-sector, startup, or highly ambiguous environments where you owned problems across multiple technical layers.
- Experience mentoring and growing engineers at multiple levels through code review, pairing, technical coaching, and example\-setting.
- Deep understanding of the software development lifecycle with examples of improving delivery practices, engineering standards, or team effectiveness.
- Experience building reusable engineering frameworks, internal developer platforms, AI enablement tools, or enterprise\-scale technical platforms.
- Familiarity with coding agents, large language model APIs, orchestration frameworks, retrieval pipelines, model evaluation approaches, and AI governance patterns.
- Ability to adapt quickly as priorities, platforms, frameworks, and product needs shift.
- Curiosity and continuous learning mindset, with the ability to stay current as AI engineering practices evolve quickly.
In addition to the salary range, this role is eligible for bonus or incentive opportunities.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-$185K range is below the median for AI Software Engineer roles in our dataset (median: $183K across 194 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,708 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Charles Schwab, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $219,250 based on 424 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($167K) sits 24% below the category median. Disclosed range: $150K to $185K.
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 Austin pay a median of $214,343 across 87 tracked positions.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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