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About This Role
Who We Are
Applied Materials is the global leader in materials science and engineering solutions that are at the foundation of virtually every new semiconductor chip and advanced display in the world. The equipment that we create and service is essential to advancing AI and accelerating the commercialization of next-generation semiconductor chips. Join us and push the boundaries of materials science and engineering in a company at the foundation of the electronics industry. The work we do together advances the world’s technology.
What We Offer
Salary:
$140,000.00 - $192,500.00 Location:
Austin,TX You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.
At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits .
Leads IT audit engagements and helps advance Applied Materials' IT audit practice, providing independent assurance over technology, cybersecurity, and AI risk across a global enterprise at the center of the semiconductor industry. A senior, hands-on manager who influences across organizational boundaries, leads cross-functional teams, directs third-party specialists, and helps drive AI adoption within Global Internal Audit (GIA).
Job Description
Global Internal Audit (GIA) is transforming how technology assurance gets done at Applied Materials, and we are hiring a Senior Internal Audit Manager to help lead that transformation. You will audit cloud, cybersecurity, and AI risk across a global manufacturing, laboratory, and R&D environment, and influence how the practice evolves — its methods, its tooling, and its scope.
IT audit carries the heaviest weighting of the role, complemented by advisory work and AI enablement. You will partner with leaders across Global Information Services (GIS), including the Chief Information Security Officer and Chief Data Officer organizations, and report to the Senior Director who leads IT Audit, Audit Analytics, and Enterprise Risk Management (ERM).
We welcome candidates who built their expertise in IT operations and infrastructure, cybersecurity, information security, technology risk, or IT audit, and are particularly interested in people who have worked both sides of the control, in operations and in assurance. You should be comfortable going a level deeper than the summary, following a technical discussion on its own terms and knowing where to press. Deep specialization in any single technology is not required.
Key Responsibilities
- Lead complex IT audits across infrastructure, network and endpoint security, identity and access management (IAM), cloud platforms and security, application controls, data protection, resilience, and site, laboratory, and OT security
- Drive risk assessment and audit planning — lead risk interviews across the business and technology organizations, prioritize the technology audit slate, and build actionable audit plans behind it
- Influence across boundaries — shape the technology risk agenda with GIS, business, and operations partners, build alignment with senior leadership, and drive a shared vision within the IT Audit team
- Lead cross-functional teams with disciplined project management, spanning GIA auditors, guest auditors from business and technical functions, and external contractors and consultants
- Direct third-party engagements — co-source partners, staff augmentation, and specialist providers across technology, security, and governance, including penetration testing, red / purple team exercises, and targeted technical assessments
- + Assure AI in use across the business, including agentic AI and citizen-developed tools, confirming it is secure, appropriately controlled, well governed, and aligned to applicable regulations
- + Advance how GIA works — apply AI across the audit lifecycle, from risk assessment and execution to continuous monitoring, control automation, reporting, quality assurance, and analytics, and help deploy modern audit methodologies and tools, including agile auditing
- Advise the business in areas such as cyber resilience, supplier cyber risk, and data governance
- Assure transformation programs — independent assurance over major technology and platform initiatives, partnering across all GIA pillars
- Coach and develop IT audit professionals, building technical depth across the team and raising the practice toward best-in-class
Functional Knowledge
- Technology risk and controls across IT operations, infrastructure, networks, endpoints, and enterprise platforms, including IT general controls (ITGC) and IT application controls (ITAC)
- Cybersecurity operations and architecture, including threat and vulnerability management, security monitoring and incident response, identity and access management, and data protection
- Cloud platforms and cloud security (MS Azure, AWS, GCP), including cloud identity, configuration management, and workload protection
- Enterprise AI architecture, including third-party foundation models, retrieval-augmented generation (RAG) pipelines, orchestration and multi-agent workflows, Model Context Protocol (MCP) servers and gateways, and AI embedded within purchased software
- AI risk and control surface, including agent and AI inventory, non-human identity and entitlements, shadow AI detection, prompt injection and data exfiltration, RAG permission inheritance, human-in-the-loop checkpoints, and traceability of agent actions
- AI and technology risk frameworks and regulation, including the NIST AI Risk Management Framework, ISO/IEC 42001, the EU AI Act, the NIST Cybersecurity Framework, ISO/IEC 27001, COBIT, and CIS Controls
- Internal audit methodology and practice, including risk-based audit planning, controls design and testing, Institute of Internal Auditors (IIA) standards, and external audit methodology and coordination
Business Expertise
- Connects technology risk to business strategy, operations, and financial outcomes, and frames it in terms leaders can act on
- Experience in complex, technology-intensive environments — semiconductor, advanced manufacturing, or other intellectual-property-intensive industries — is a plus
Leadership
- A proven influencer who builds alignment without direct authority across technology, security, business, and operations, and earns the confidence of senior leadership
- Earns credibility with technical partners through genuine domain understanding, and converts that credibility into influence
- Sets a high bar for integrity, technical quality, and ownership, and champions new technology, AI, and modern methods in IT Audit
Problem Solving
- Translates ambiguous direction into well-scoped plans, focuses deep technical effort where risk concentrates, and applies sound judgment in emerging areas such as AI where established practice is still forming
Impact
- Strengthens the assurance Applied obtains over its technology, cybersecurity, and AI risk, and influences how that risk is prioritized and remediated across the global technology organization
Interpersonal Skills
- Explains complex technical risk clearly to audiences from subject matter experts to executive leadership, defends conclusions on their merits under detailed technical questioning, and builds lasting, trust-based relationships
Education & Experience
- Bachelor's degree in Management Information Systems, Computer Science, Cybersecurity, Engineering, or a related field; master's degree in a technical discipline preferred
- 10+ years of progressive experience in IT operations and infrastructure, cybersecurity, information security, technology risk, security operations, or IT audit, including leading complex technology audits or security and control programs, leading cross-functional teams, and directing third-party specialists; Big 4 or comparable technology or cyber risk experience preferred
- Professional certification strongly preferred, such as CISA, CISSP, CISM, CRISC, CCSP, CGEIT, PMP, or ISACA Advanced in AI Audit (AAIA)
Education — Minimum: Bachelor's Degree
Education — Field of Study: Management Information Systems; Computer Science; Cybersecurity; Engineering; or related
Years of Experience: 10+
Certifications (Preferred): CISA · CISSP · CISM · CRISC · CCSP · CGEIT · PMP · ISACA Advanced in AI Audit (AAIA)
Skills: IT Audit · Internal Audit · Technology Risk · Cybersecurity · Information Security · IT General Controls (ITGC) · Cloud Security · Identity and Access Management (IAM) · Risk Assessment · Internal Controls · IT Infrastructure · Security Operations · Vulnerability Management · Penetration Testing · Incident Response · Third-Party Risk Management · Audit Planning · NIST Cybersecurity Framework · ISO 27001 · COBIT · SOC 2 · Artificial Intelligence (AI) · AI Governance · Data Governance · Application Security · Operational Technology (OT) Security · Microsoft Azure · Amazon Web Services (AWS) · Project Management · Stakeholder Management
Languages: English
Additional Information
Time Type:
Full time Employee Type:
Assignee / Regular Travel:
Yes, 20% of the Time Relocation Eligible:
Yes The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.
For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.
Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.
In addition, Applied endeavors to make our careers site accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at [email protected], or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.
Salary Context
This $140K-$192K range is below the median for AI/ML Engineer roles in our dataset (median: $178K across 2239 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 11,520 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Applied Materials, 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 $212,040 based on 13,780 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($166K) sits 22% below the category median. Disclosed range: $140K to $192K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,250; VP: $235,830.
Applied Materials AI Hiring
Applied Materials has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Austin, TX, US, Santa Clara, CA, US. Compensation range: $192K - $300K.
Location Context
AI roles in Austin pay a median of $208,000 across 317 tracked positions. That's 3% below 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 11,520 open positions tracked in our dataset. By seniority: 140 entry-level, 5,630 mid-level, 4,176 senior, and 1,574 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (708 positions). The remaining 10,737 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,000. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 11,520 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,769), AI Software Engineer (586), AI Product Manager (511). 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 (140) are outnumbered by mid-level (5,630) and senior (4,176) 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 1,574 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (708 positions), with 10,737 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 $213,480. Top-quartile roles start at $264,000, 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 $266,050 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 (2,267 postings), Aws (1,178 postings), Azure (972 postings), Rag (924 postings), Gcp (715 postings), Pytorch (586 postings), Claude (572 postings), Kubernetes (559 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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