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About This Role
Austin, TX
Requisition ID 2026\-122695 Category Engineering \& Software Development Position type Regular Pay range USD $125,000\.00 \- $140,000\.00 / Year Application deadline 2026\-06\-20
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 Technology Services enables the future of how clients manage their money by delivering innovative, secure, and scalable technology solutions that support investing and financial planning at scale. Within this environment, the BPMT Departmental Pega One (DPO) platform plays a critical role in modernizing and streamlining key business workflows across functions such as account maintenance, retirement services, fund maintenance, custody, and operations.
As a Senior Specialist, Software Development Engineer, you will contribute to building and evolving full\-stack applications that drive operational efficiency and improve business outcomes. This role focuses on developing high\-quality, high\-performance solutions using modern technologies while enabling seamless integrations and reusable components across the enterprise. You will partner closely with architects, product owners, and cross\-functional stakeholders to design, build, and deliver solutions that meet business needs while maintaining strong standards for security, scalability, and reliability.
You will operate in a fast\-paced, Agile environment, leveraging AI\-powered engineering practices—including tools such as GitHub Copilot and custom AI agents—to accelerate development, enhance productivity, and improve code quality. Your impact will be measured by your ability to deliver end\-to\-end features, proactively identify and mitigate risks, and ensure solutions are production\-ready through robust testing, CI/CD automation, and release management practices. In addition, you will help elevate team performance by mentoring others, contributing to engineering standards, and continuously improving development and delivery processes.Key Responsibilities* Design, build, test, and maintain software applications across the full stack under the guidance of senior engineers.
- Use GenAI tools (for example, GitHub Copilot, Cursor, Claude Code) to accelerate coding, debugging, and documentation.
- Take full ownership of all code changes, including those generated with AI tools.
- Review, test, and thoroughly understand every change before it is merged and released to production.
- Write clean, maintainable, and well\-structured code with or without AI assistance.
AI\-Assisted Workflows
- Create clear prompts, task definitions, and acceptance criteria to effectively direct AI agents on your own work.
- spec\-driven development practices to translate requirements into structured, AI\-ready specifications.
- Contribute to AI agent development by helping define agent goals, tool use, and simple task flows under the guidance of senior engineers.
- Review and validate AI\-generated code for correctness, quality, security, and maintainability.
- judgment to accept, modify, or reject AI suggestions based on context and team standards.
Testing and Quality
- Generate and improve unit, integration, and regression tests using AI\-assisted tools.
- Ensure test coverage meets team standards before code is merged.
- Identify and address security, performance, and reliability concern early in the development cycle.
Modernization* Assist in refactoring and modernizing existing codebases using AI\-assisted workflows to improve readability, maintainability, and performance.
- Help identify technical debt and contribute to AI\-accelerated remediation efforts under senior guidance.
Collaboration* Participate in architecture discussions, design reviews, and code reviews to learn and contribute to technical direction and quality standards.
- Help maintain custom instructions and prompt engineering guidelines that support consistent, effective AI\-assisted development across the team.
- Continuously improve your AI\-enabled engineering practices and share learnings, patterns, and tooling insights with peers.
What you have
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Required Qualifications* Bachelor’s degree in computer science, Information Technology, or a related field
- 5\+ years of experience in full\-stack software development within enterprise environments
- Strong proficiency in object\-oriented programming languages such as Java
- Hands\-on experience building modern web applications using React and Node.js
- Experience designing and developing multi\-tier or n\-tier enterprise applications
- Demonstrated experience with AI\-assisted development tools (e.g., GitHub Copilot), including the use of custom agents or AI\-enabled development practices
- Experience building and maintaining CI/CD pipelines using tools such as GitHub Actions
- Proven ability to develop and execute automated testing strategies, including unit, integration, and regression testing
- Solid foundation in computer science fundamentals, including:
o Algorithms and data structures
o Object\-oriented programming and common design patterns
o Basic system design
o Databases and networking
o Concurrency fundamentals
o Testing and CI/CD
o Secure coding principles
- Hands\-on experience using GenAI coding tools (GitHub Copilot, Cursor, or similar) in day\-to\-day development.
- Proficiency in Java or .NET.
- Ability to critically review AI\-generated code for bugs, security issues, and code quality.
- Exposure to agentic workflows and spec\-driven development concepts, with the ability to translate basic design artifacts into AI\-ready task specifications.
- Practical experience with prompt engineering and authoring custom instructions to improve AI\-assisted development outcomes.
- Independently applies application maintenance practices and collaborates to ensure continuous support
- Tracks data pipelines and stores to support analytics and machine learning
- Experience supporting and participating in production release activities
- Working knowledge of SQL and/or NoSQL databases, including query optimization
- Strong understanding of Agile development methodologies and practices
- Demonstrated problem\-solving, debugging, and decision\-making skills in complex technical environments
- Strong collaboration, communication, and stakeholder engagement skills
- Knowledge of Retirement Business Services (RBS) workflows and business processes
Preferred Qualifications* Experience developing applications on the Pega platform
- Exposure to building or extending AI agents using frameworks such as LangChain, Copilot Workspace, AutoGen, CrewAI, or similar.
- Experience with at least one cloud platform (GCP, AWS, or Azure).
- Familiarity with secure coding practices.
- Experience contributing to modernization of legacy applications.
- Familiarity with distributed systems and large\-scale enterprise architecture
- Experience with federated identity and SSO standards such as SAML or OAuth
- Exposure to cloud platforms and cloud\-native development practices
- Experience designing and implementing REST or SOAP\-based APIs
- Knowledge of test\-driven development approaches such as ATDD or BDD
- Experience contributing to software design standards, architecture documentation, and solution governance
- Ability to evaluate and recommend technology solutions and platform improvements
- Strong ability to solve complex, time\-sensitive problems with innovative and scalable solutions
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 $125K-$140K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($132K) sits 27% below the category median. Disclosed range: $125K to $140K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Charles Schwab AI Hiring
Charles Schwab has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Southlake, TX, US, Austin, TX, US. Compensation range: $94K - $220K.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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