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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:
$218,500.00 - $300,500.00 Location:
Santa Clara,CA 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 .
Position Summary
We are developing a single AI platform that serves multiple business units, running inside an air-gapped environment.
This role owns the architecture and the technical delivery of the platform. You will interface with senior architects and principal AIML Ops engineers, set technical direction, work across business units to turn business problems into common platform capability, and stay hands-on to lead development.
Core Responsibilities
- Platform architecture. Own the target-state reference architecture: domain-driven decomposition, bounded contexts, ports and adapters, API-first and event-driven boundaries, documented as C4 views and decision records.
- Deployment flexibility. Architect one system that runs on premises in restricted networks, in private cloud, and as a multi-tenant service in public cloud, with hosting model set by configuration rather than a fork.
- Cloud-native runtime. Set the Kubernetes and microservices standard: Helm packaging, operator patterns, GitOps reconciliation, service mesh where justified, and the scale, caching and sharding strategy for the hosted path.
- Embedded AI. Define the AI architecture for models running on customer hardware and in our cloud: open-weight model selection and quantization, GPU inference serving with continuous batching, model registry and versioning.
- Grounding and guardrails. Own the retrieval-augmented generation design covering embeddings, vector store, hybrid retrieval and reranking, with tool-use patterns that are deterministic, bounded and fully traceable.
- AI assurance. Establish evaluation and model-risk practice: golden datasets, evaluation harnesses, groundedness metrics, drift detection, prompt-injection defenses, and a clear position on build versus adapt.
- Pipeline ownership. Own CI, CD and MLOps end-to-end: trunk-based development, automated testing and quality gates, container scanning and SBOM, progressive delivery, infrastructure as code, model lineage and drift monitoring.
- Technical delivery. Hold the teams to the architecture and to the date. Own release sequencing across squads, dependency and risk management, capacity planning, and the DORA metrics that show delivery health.
- Business to architecture translation. Work with business units and customers to understand process, constraints and pain points, then convert them into domain models, capability roadmaps and architecture decisions.
- Cross-BU leadership. Serve as senior technical interface to product management, field engineering, manufacturing systems, data science, IT and InfoSec. Chair architecture governance and hold one sequenced roadmap.
- Configurability. Own the extension model: plugin and adapter interfaces, versioned APIs with backward compatibility guarantees, declarative configuration, and tenant isolation across silo, bridge and pool patterns.
- Systems integration. Architect integration to equipment and factory automation interfaces, MES, EDA and yield management systems, historians and data lakes, behind stable anti-corruption layers.
- Non-functional ownership. Own SLOs and error budgets, OpenTelemetry observability, resilience patterns such as circuit breaker and bulkhead, zero-trust segmentation, and data governance for residency, retention and egress.
- Hands-on leadership. Stay credible across the technology stack, including Java and Spring Boot, Python, Node.js, .NET, React and Angular, and lead development directly where the risk warrants it.
- Modernization. Own strangler-fig migration of legacy systems onto the common platform, a published deprecation policy, and a funded technical debt register.
- Organization. Interface with managers and principal engineers across different teams and geographical sites.
Required Qualifications
- Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering or a related field. Master’s preferred.
- 15+ years of experience in software engineering, including 8+ years in architecture roles and 5+ years leading engineering organizations with managers reporting in.
- Demonstrated ownership of a distributed platform architecture at scale, covering domain decomposition, API and event contract management, resilience and consistency strategy, multi-tenancy and tenant isolation.
- Hands-on architecture experience across public cloud (AWS and/or Azure) and Kubernetes or OpenShift, including container packaging, orchestration, GitOps and lifecycle management.
- Experience architecting for both on-premises customer-controlled deployments and cloud-hosted delivery, including the trade-offs between them and the realities of operating under customer security governance.
- Fluency in architecture documentation and governance practice: C4 or equivalent modelling, architecture decision records, design review standards, fitness functions, NFR definition and technical debt management.
- Proven accountability for delivering a platform to committed dates across multiple squads, covering release planning, dependency and risk management, capacity planning and quality ownership. Architecture authority without delivery accountability is not sufficient for this role.
- Direct experience architecting and operating CI/CD pipelines and a production MLOps lifecycle and using engineering metrics such as DORA to diagnose and improve delivery performance.
- Deep hands-on capability across the technology stack, with the credibility to set the reference implementation, review design and code, and lead development efforts directly. This role is not purely advisory.
- Experience running a large multi-stack application portfolio to common engineering standards, and executing migration of legacy systems onto a shared platform.
- Practical experience deploying machine learning or LLM-based capability to production, including GPU capacity planning, inference serving and model optimization for constrained environments.
- Demonstrated ability to elicit and structure requirements directly from business and customer stakeholders through process mapping, current and future state analysis, use-case definition and workflow analysis, and to translate them into domain models, capability maps and architecture decisions.
- Track record of building a shared platform across business units with competing priorities, converging overlapping requirements into common capability and sustaining that alignment when local incentives pull against it.
- Ability to separate genuine platform capability from customer or BU-specific customization, and to design the configuration and extension model that absorbs the difference without forking the codebase.
- Experience building and defending the business case for platform investment in commercial terms such as engineering cost avoided, customer time to value, deployment cycle time and compliance exposure.
- Comfort operating in customer-facing technical settings, including requirements workshops, design reviews, security assessments and qualification discussions.
- Semiconductor industry experience in fab operations, equipment software, process control, yield analysis, manufacturing systems or design enablement, with enough depth to reason about customer process problems and not only their software symptoms. This is a hard requirement.
Preferred Qualifications
- Direct engagement with leading logic and memory fabs as a software supplier, including participation in security and qualification reviews.
- Experience taking an on-premises product to a multi-tenant SaaS delivery model, including the tenancy, metering, onboarding and operational changes that requires.
- Experience with digital twin, simulation or virtual metrology software in a manufacturing context.
- Familiarity with semiconductor data and equipment integration standards and factory automation interfaces.
- Experience with open-weight model deployment, GPU cluster scheduling and inference optimization frameworks.
- Prior ownership of a reusable component library or internal developer platform adopted across a large application portfolio.
- Experience establishing AI governance covering model risk management, acceptable use policy and evaluation standards in a regulated or IP-sensitive enterprise.
- Background in developer enablement at scale, including technical community building, hackathons, champion networks and measured adoption of engineering tooling.
Competencies
Architectural judgment. Knows which decisions are expensive to reverse, and which are not, and spends rigor accordingly.
Business translation. Hears a process problem described in the customer’s language and returns a design that solves it, without mistaking the stated request for the underlying need.
Delivery ownership. Treats the date and the quality bar as the architect’s problem too, not the program managers alone.
Technical credibility. Earns the respect of senior engineers by being able to do the work, not only direct it.
Influence without authority. Moves stakeholders who do not report to this role toward a shared platform and keeps them aligned when local incentives pull the other way.
Honesty about AI. Applies AI where it produces measurable outcomes and declines it where a deterministic method is better.
Position requires understanding of Applied Materials global Standards of Business Conduct and compliance with these standards at all times. This includes demonstrating the highest level of ethical conduct reflecting Applied Materials' core values.
Additional Information
Time Type:
Full time Employee Type:
Assignee / Regular Travel:
Yes, 25% 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 $218K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($259K) sits 22% above the category median. Disclosed range: $218K to $300K.
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.
Applied Materials AI Hiring
Applied Materials has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Santa Clara, CA, US. Compensation range: $300K - $300K.
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/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 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).
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 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.
Frequently Asked Questions
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