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About This Role
Austin, TX
Requisition ID 2026\-122496 Category Engineering \& Software Development Position type Regular Pay range USD $175,000\.00 \- $220,000\.00 / Year Application deadline 2026\-06\-16
Your opportunity
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At Schwab, you will build a rewarding career while making a difference in the lives of our millions of clients. Here, innovative thinking meets creative problem solving as we work together to challenge the status quo. Joining Schwab means joining a company committed to transforming the financial industry and putting clients at the center of everything we do.
Schwab’s AI Strategy \& Transformation team, known as AI.x, is the central hub for Artificial Intelligence at Schwab. We are an integrated product, engineering, strategy and risk team, all based in San Francisco. We help set the enterprise vision for AI, invest in the most promising opportunities, and accelerate delivery across the company. We also build the core platform that powers AI at scale and explore next\-generation GenAI efforts that will redefine how we serve our clients. As a Senior Engineer on AI.x, you will play a key role in bringing these priorities to life by designing and delivering innovative AI solutions.
This role is an opportunity to join a high\-profile team shaping Schwab’s future with AI, to build solutions that matter to millions of clients, and to grow your career in one of the most exciting areas of technology today.
As a Senior AI Site Reliability Engineer you will support reliability efforts for cutting\-edge GenAI applications that enhance the client experience and create value. You will work closely with architects and engineers to ensure scalability, reliability and security of solutions that build towards an enterprise strategy. You will lead automation\-first initiatives, build robust CI/CD pipelines for one\-touch deployments, and implement comprehensive observability frameworks to minimize MTTD and MTTR. This role requires participation in on\-call rotations to ensure 24/7 reliability of critical AI systems. Above all, you will the rigor, discipline, and technical depth to help shape the next generation of AI at Schwab.
Roles \& Responsibilities:* Lead automation\-first initiatives to eliminate toil and manual interventions, defining and executing the strategic roadmap for reliability, observability, and self\-healing systems across AI.x platforms
- Design and implement robust CI/CD pipelines enabling one\-touch deployments with automated testing, validation, and rollback capabilities to accelerate delivery velocity and reduce deployment risk
- Implement comprehensive observability frameworks for real\-time monitoring of AI services, including metrics, logs, and traces, with intelligent alerting and automated diagnostics to minimize MTTD and MTTR
- Participate in on\-call rotation providing 24/7 support for production AI systems, ensuring rapid incident response, root cause analysis, and resolution with measurable SLO targets
- Establish and manage Service Level Objectives (SLOs), Service Level Indicators (SLIs), error budgets, and incident response runbooks to drive continuous reliability improvements
- Champion Infrastructure\-as\-Code (IaC) practices and automate environment provisioning, configuration management, and deployment processes to ensure consistency, repeatability, and operational efficiency
- Collaborate seamlessly with AI Engineering teams to integrate SRE practices early in the development lifecycle, promoting a culture of reliability and shared responsibility
- Proactively identify and resolve reliability, performance, and scalability issues through data\-driven analysis, capacity planning, and system optimization
- Implement and maintain monitoring, alerting, and incident response frameworks to ensure system health and reliability, maximizing production availability
- Champion reliability, monitoring, observability, and operational best practices for AI systems and data pipelines, establishing patterns and standards for the organization
What you have
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Required Qualifications* 8\+ years of software engineering experience, with 4\+ years as a hands\-on Site Reliability Engineer in startups and/or large organizations.
- Bachelor’s degree in Computer Science or related field, or equivalent experience.
- 5\+ years building complex products from scratch, running them in production, and ensuring operational reliability.
- 3\+ years working with containers and cloud\-native applications, operationalizing them in the public cloud with infrastructure as code and CI/CD pipelines.
- 3\+ years of experience working in high\-availability hybrid\-cloud environments.
Preferred Qualifications* Strong computer science fundamentals and experience across the tech stack.
- Experience with proprietary or open\-source LLMs (e.g., Gemini, Claude, OpenAI), deploying LLM\-powered applications to production and maintaining availability.
- Strong written and verbal communication skills to clearly convey ideas and feedback.
- Strong understanding of observability, incident management and reliability engineering principles.
- Mindset of continuous learning and improvement, adept at both giving and receiving feedback.
- Ability to troubleshoot complex problems with ambiguous or incomplete data in distributed systems.
- Curiosity about new technologies and processes, proactively sharing knowledge and seeking improvement.
- Experience with Terraform and Google Cloud Platform.
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 $175K-$220K range is above the median 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 ($197K) sits 9% above the category median. Disclosed range: $175K to $220K.
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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