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About This Role
Ann Arbor, MI ; Southlake, TX ; Austin, TX Requisition ID 2026-127034 Category Engineering & Software Development Position type Regular Pay range USD $150,000.00 - $180,000.00 / Year Application deadline 2026-10-20
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). Schwab Technology Services enables the future of how clients manage their money through innovative and reliable technology products and services. As a Senior Manager, Software Development and Engineering within the thinkorswim Service Availability Engineering (SAvE) team, you will design and deliver scalable, production-grade artificial intelligence solutions that improve client experiences and support data-informed business decisions. You will use analytical thinking and problem-solving skills to build Python-based data pipelines, integrations, and cloud solutions that connect enterprise applications with Generative AI and large language model capabilities. In collaboration with architects, engineers, data scientists, and business partners, you will translate complex needs into secure, reliable systems. Your technical leadership will strengthen testing, monitoring, observability, and operational practices while developing others through mentorship and knowledge sharing. Your work will help move emerging AI concepts into production and deliver consistent business and client value. What you have
Required Qualifications
- Bachelor’s degree in computer science, data engineering, engineering, mathematics, analytics, or a related field, or equivalent work experience.
- 8+ years of software engineering, data engineering, or related experience, including delivering enterprise-scale solutions.
- Advanced Python development experience focused on data processing, system integrations, and pipeline development.
- Experience designing, developing, and supporting trading applications in cloud environments.
- Hands-on experience with Google Cloud Platform services such as Cloud Run, Google Cloud Storage, Dataflow, or comparable cloud-native technologies.
- Experience integrating APIs, distributed systems, and enterprise applications.
- Demonstrated Data Accuracy Management skills, including implementing validation, quality controls, and practices that protect data integrity throughout its lifecycle.
- Strong Problem Solving and Technical Troubleshooting skills, including debugging complex production issues, conducting root cause analysis, and implementing corrective actions.
- Experience developing automated testing strategies, creating test data, validating outputs, and using modern source-control practices.
Preferred Qualifications
- Experience integrating Generative AI and large language model capabilities into enterprise applications.
- Experience working with vendor-provided AI platforms and cloud-based AI services.
- Understanding of AI application architecture, including data preparation, testing, deployment, monitoring, and operational support.
- Experience developing scalable data platforms that support AI-enabled products and services.
- Demonstrated Cloud Services Management and DevOps skills used to build observable, resilient, and highly available production systems.
- Strong Cross-Functional Collaboration skills, with the ability to align technical solutions with business and client outcomes.
- Demonstrated Adaptability when managing changing priorities, emerging technologies, and interconnected systems.
- Experience Developing Others through mentoring, knowledge sharing, and the promotion of software quality and engineering best practices.
In addition to the salary range, this role is 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 $150K-$180K range is below the median for AI Software Engineer roles in our dataset (median: $192K across 198 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 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% 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 $220,000 based on 1,601 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($165K) sits 25% below the category median. Disclosed range: $150K to $180K.
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 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 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).
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 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.
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