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About This Role
Company Overview
KLA is a global leader in diversified electronics for the semiconductor manufacturing ecosystem. Virtually every electronic device in the world is produced using our technologies. No laptop, smartphone, wearable device, voice\-controlled gadget, flexible screen, VR device or smart car would have made it into your hands without us. KLA invents systems and solutions for the manufacturing of wafers and reticles, integrated circuits, packaging, printed circuit boards and flat panel displays. The innovative ideas and devices that are advancing humanity all begin with inspiration, research and development. KLA focuses more than average on innovation and we invest 15% of sales back into R\&D. Our expert teams of physicists, engineers, data scientists and problem\-solvers work together with the world’s leading technology providers to accelerate the delivery of tomorrow’s electronic devices. Life here is exciting and our teams thrive on tackling really hard problems. There is never a dull moment with us.Group/Division
With over 40 years of semiconductor process control experience, chipmakers around the globe rely on KLA to ensure that their fabs ramp next\-generation devices to volume production quickly and cost\-effectively. Enabling the movement towards advanced chip design, KLA's Global Products Group (GPG), which is responsible for creating all of KLA’s metrology and inspection products, is looking for the best and the brightest research scientist, software engineers, application development engineers, and senior product technology process engineers. The Broadband Plasma Division (BBP) provides market\-leading patterned wafer optical inspection systems for leading\-edge IC manufacturing. Logic, foundry, and memory customers depend on BBP products to detect yield\-critical defects for process debug and excursion monitoring at advanced process nodes. BBP flagship products include the 29xx and 39xx series which leverage Broadband Plasma technology to capture a wide range of defects with ultimate sensitivity at the optical inspection speeds needed for inline defect monitoring.Job Description/Preferred Qualifications
We are looking for a full\-time Deep Learning Algorithm Engineer who is passionate about pioneering Deep Learning (DL), foundation models, and GenAI for image processing and computer vision applications in the semiconductor process control business.
Qualified candidates are expected to have a strong background and in\-depth experience in deep learning, especially in object detection, segmentation, vision foundation models, and multimodal models. Candidates should also have a deep understanding of relevant theory and hands\-on experience grounding DL/GenAI models in real application domains, with strong emphasis on performance, efficiency, and deployment.
The ideal candidate can work independently across the full deep learning project lifecycle, including conceptualizing, exploring, designing, implementing, optimizing, and deploying models. Responsibilities for this position include, but are not limited to:
- Understand state\-of\-the\-art (SOTA) deep learning and GenAI models.
- Connect SOTA DL modeling approaches to domain problem statements.
- Analyze modeling requirements based on product feature requirements.
- Design deep learning and GenAI models to meet modeling requirements.
- Implement modeling prototypes and perform analysis.
- Perform model training and/or tuning on domain datasets.
- Evaluate and validate model performance against defined metrics.
- Analyze model performance bottlenecks.
- Design and optimize DL model architectures, including new modules, efficient backbones, and model compression techniques (e.g., distillation).
- Optimize DL or GenAI model throughput and cost, including mixed\-precision and low\-precision inference and training (e.g., FP16, FP8\).
- Work and communicate collaboratively with peers.
- Present ideas, concepts, and results in professional technical settings.
Qualifications/Education Desired
- Ph.D. in Electrical Engineering, Computer Science, or related quantitative fields.
- Academic or industrial experience applying deep learning or GenAI to real\-world problem(s), with impactful results.
- In\-depth experience developing and optimizing deep learning, Vision Foundation Models (VFM), or Vision Language Models (VLM) in at least one of the following areas: computer vision, image processing, robotics, NLP, or equivalent, with strong emphasis on efficiency, scalability, and deployment performance.
- Required experience with DL model optimization/distillation for mixed or reduced precision (e.g., FP16, FP8\) to improve throughput, latency, and deployment efficiency.
- Experience with GenAI coding tools, vibe coding, or vibe engineering.
- Proficiency in Python and one additional programming language from: C\+\+, Java, Rust, Go.
- Proficiency in at least one deep learning framework (e.g., PyTorch, TensorFlow, JAX, or equivalent).
- Demonstrated deep learning expertise via technical publications in top conferences (e.g., NeurIPS, CVPR, ICML, ICLR, KDD, SIGGRAPH, etc.) and/or industrial patents and/or impactful open\-source projects is required.
- Travel required: up to 10%.
- Experience in semiconductor process control is a plus.
Minimum Qualifications
- Doctorate (academic) degree with 0 years of related work experience; or Master’s degree with 3 years of related work experience.
- Academic or industrial experience applying deep learning or GenAI to real\-world problem(s), with impactful results.
- Required experience with DL model optimization/distillation for mixed or reduced precision (e.g., FP16, FP8\) to improve throughput, latency, and deployment efficiency.
- Travel required: up to 10%.
Base Pay Range: $136,300\.00 \- $231,700\.00 Annually
Primary Location: USA\-CA\-Milpitas\-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.
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.
Be aware of potentially fraudulent job postings or suspicious recruiting activity by persons that are currently posing as KLA employees. KLA never asks for any financial compensation to be considered for an interview, to become an employee, or for equipment. Further, KLA does not work with any recruiters or third parties who charge such fees either directly or on behalf of KLA. Please ensure that you have searched KLA’s Careers website for legitimate job postings. KLA follows a recruiting process that involves multiple interviews in person or on video conferencing with our hiring managers. If you are concerned that a communication, an interview, an offer of employment, or that an employee is not legitimate, please send an email to [email protected] to confirm the person you are communicating with is an employee. We take your privacy very seriously and confidentially handle your information.
Salary Context
This $136K-$231K range is above the median for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000. Disclosed range: $136K to $231K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
KLA AI Hiring
KLA has 3 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Milpitas, CA, US, Ann Arbor, MI, US. Compensation range: $190K - $238K.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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