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About This Role
To make electronics, you need chips, wafers, transistors, reticles, and... To make these, you must see, test and manufacture them at scale—faster and better than ever before. That's where KLA comes in. Whether you're early in your career or an experienced professional, you'll solve complex challenges, work alongside brilliant minds and help shape the future of technology.
Group/Division
The KLA Services team consists of Service Sales, Marketing, Spares Supply Chain Management, Field Operations, Engineering, Product Training, Digital Solutions and Analytics, and Technical Product Support. Our services organization maximizes the value of our customers' KLA assets – with highly trained service and product support engineers providing installation services and 24/7 technical support and parts delivery through our extensive supply chain network. What You'll Do
In this role, you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise.
Business Engagement & Opportunity Discovery (Primary Focus)
- Partner closely with NPI leaders, subject matter experts, and operational teams to understand business processes, priorities, and pain points.
- Identify opportunities where AI, automation, analytics, or digital solutions can improve efficiency, productivity, quality, or decision-making.
- Analyze current workflows and recommend innovative approaches to simplify operations and enhance business performance.
- Serve as a trusted advisor to stakeholders on evaluating and applying AI technologies to solve operational challenges.
- Continuously develop domain expertise in NPI operations to identify high-value opportunities for business transformation.
AI Solution Design & Rapid Prototyping (Primary Focus)
- Translate business challenges into solution concepts, proof-of-concepts, and rapid prototypes.
- Develop lightweight AI-enabled applications, copilots, agents, workflow automations, and decision-support tools.
- Leverage modern AI technologies, large language models, enterprise knowledge systems, and AI-assisted development tools to accelerate solution delivery.
- Validate technical feasibility, business value, and user adoption through iterative experimentation and stakeholder feedback.
- Collaborate with Data Scientists to incorporate predictive models, analytics, and business insights into user-facing solutions.
Business Adoption & Value Realization (Primary Focus)
- Drive pilot deployments and user adoption efforts to ensure solutions deliver measurable business outcomes.
- Gather stakeholder feedback and continuously refine solutions based on real-world usage.
- Define and monitor business success metrics, user engagement, and operational impact.
- Promote self-service capabilities that increase access to knowledge, insights, and decision-support tools across the organization.
Product & Solution Management (Secondary Focus)
- Help prioritize AI opportunities based on business value, technical feasibility, and organizational impact.
- Develop lightweight solution roadmaps and implementation plans.
- Document business requirements, user feedback, and lessons learned from prototype development.
- Serve as the bridge between business stakeholders, Data Scientists, and AI engineering teams during productionization.
- Support the transition of successful prototypes into scalable enterprise solutions.
Preferred Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, Data Science, Business Analytics, or a related discipline.
- Experience developing software applications, business solutions, process automation, or digital transformation initiatives.
- Familiarity with AI technologies including large language models, retrieval systems, agents, workflow automation, or related technologies.
- Experience with Python, Power Platform, Copilot Studio, Azure AI, SQL, or similar technologies.
- Strong analytical and problem-solving skills with the ability to navigate ambiguous business challenges.
- Excellent communication and stakeholder engagement skills.
- Ability to influence without authority and work effectively across technical and non-technical teams.
- Curiosity, creativity, and a passion for improving business processes through technology.
What Success Looks Like
- NPI teams spend less time on manual and repetitive activities.
- Critical knowledge becomes easier to access and reuse.
- AI-enabled solutions are broadly adopted by users.
- Business decisions are made faster with better information.
- High-value prototypes successfully transition into enterprise-scale solutions.
- Measurable operational improvements are achieved through AI-driven innovation.
Impact
This role serves as the bridge between business challenges and AI-enabled solutions. Success is measured not by the number of applications developed, but by the ability to identify meaningful opportunities, rapidly validate new ideas, improve operational effectiveness, and drive measurable business outcomes across the NPI organization.
Expected Time Allocation
80% Business Engagement & Solution Development
- Understanding business operations
- Stakeholder partnership
- Opportunity discovery
- AI solution design
- Prototyping
- Adoption and value realization
20% Product Management
- Prioritization
- Roadmapping
- Requirements definition
- Success metrics
- Productionization coordination
Minimum Qualifications
Bachelor's or Master's degree in Computer Science, Software Engineering, Information Systems, Data Science, or a related field.
About KLA
We provide advanced inspection tools, metrology systems, process solutions, and computational analytics that make electronics possible, tackling complex challenges. From electron and photon optics to machine learning and data analytics, we seek perfection at the most fundamental level of matter in the universe. If you want to make electronics that push industries forward and make the world a better place, join us.
Total Rewards
Base Pay Range: $94,100.00 - $138,033.00 Annually
Primary Location: USA-MI-Ann Arbor-KLA KLA’s total rewards package for employees may also include participation in performance incentive programs and eligibility for additional benefits including but not limited to: medical, dental, vision, life, and other voluntary benefits, 401(K) including company matching, employee stock purchase program (ESPP), student debt assistance, tuition reimbursement program, development and career growth opportunities and programs, financial planning benefits, wellness benefits including an employee assistance program (EAP), paid time off and paid company holidays, and family care and bonding leave.
Interns are eligible for some of the benefits listed. Our pay ranges are determined by role, level, and location. The range displayed reflects the pay for this position in the primary location identified in this posting. Actual pay depends on several factors, including state minimum pay wage rates, location, job-related skills, experience, and relevant education level or training. We are committed to complying with all applicable federal and state minimum wage requirements where applicable. If applicable, your recruiter can share more about the specific pay range for your preferred location during the hiring process.
Use of AI Statement
At KLA, our interviews seek to understand your individual skills, problem-solving approach and authentic thinking. To ensure a fair and consistent evaluation, the use of AI, recording tools or other technologies to generate, suggest or provide responses during interviews—whether virtual or in person—is not permitted unless explicitly approved in advance as part of a reasonable accommodation or invited by the interviewer. Use of these tools may interfere with our ability to evaluate your individual qualifications and affect your candidacy. KLA is committed to advancing innovation through responsible AI, and we value candidates who share this mindset.
Equal Opportunity Statement
KLA is proud to be an Equal Opportunity Employer. We will ensure that qualified individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us at [email protected] or at +1-408-352-2808 to request accommodation.
For additional information, view the US Know Your Rights poster on the U.S. Equal Employment Opportunity Commission website.
Salary Context
This $94K-$138K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At KLA, 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 $212,000 based on 11,866 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($116K) sits 45% below the category median. Disclosed range: $94K to $138K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
KLA AI Hiring
KLA has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Ann Arbor, MI, US, Milpitas, CA, Ann Arbor, MI. Compensation range: $138K - $138K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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