Algorithm Engineering Intern (AI, Computer Vision & Software Engineering)

We need to output HTML fragment with sections: About the role? Actually we have content: description of role, responsibilities, required qualifications, preferred qualifications, what you'll gain, minimum qualifications, about KLA, total rewards (pay, benefits). We must use

for section headings: e.g., About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. We need to map content accordingly. We should not repeat job title. No Job ID. Remove boilerplate (cookie/navigation/legal/EEO etc). So we need to strip equal opportunity statement, use of AI statement, etc. We need to preserve substantive info. We need sections only when posting has real specific content. So we need sections like About the role (intro paragraph), Responsibilities (list), Requirements (maybe required qualifications?), Qualifications (maybe preferred?), Skills (maybe programming languages?), Benefits, Pay, Schedule (if present). There's no schedule info. So we should not include Schedule heading. Pay: we have Base Pay Range: $58.00 - $58.00 per hour based on pursuit of a Ph.D. So we can include Pay section with that info. Benefits: we have total rewards package description: medical, dental, vision, life, etc, 401K matching, ESPP, student debt assistance, tuition reimbursement, development opportunities, financial planning, wellness, EAP, paid time off, paid company holidays, family care and bonding leave. Interns eligible for some benefits. So we can include Benefits section with list. We also have About KLA description; could be About the role? Actually "About KLA" is company description. Could be placed under About the role? The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can have About the role (intro about role and division). Then Responsibilities (list). Then Requirements (maybe required qualifications). Then Qualifications (maybe preferred qualifications). Then Skills (maybe programming languages? but we already have required qualifications includes programming skills; maybe we can put Skills as separate list of technical skills: Machine Learning, Deep Learning, C++, Python, PyTorch, TensorFlow, etc). But we must not invent content; we can extract from responsibilities and qualifications. We need to preserve all substantive info; not invent. So we can include Skills section listing specific skills mentioned: Machine Learning, Deep Learning, image transformation, defect detection, analyzing large-scale image datasets, evaluating algorithm performance, prototyping algorithms using Python, PyTorch, TensorFlow, developing production-quality software using Modern C++ and Python, software design, testing, debugging, code reviews, version control, collaboration. But that may be redundant with Responsibilities. However we can still include Skills if we have content. The instruction: "Prefer a few substantive sections over many thin ones." So maybe we combine Responsibilities and Skills into one? But we need to follow headings list; we can include only those headings that have real content. We could omit Skills if we think it's thin. But we have content for Skills (programming languages, ML frameworks). Could be considered substantive. We also have Requirements (maybe minimum qualifications). We have Minimum Qualifications: pursuing a Ph.D. in certain fields. Also Required Qualifications: strong background in CV, ML, etc; strong programming skills in C++/Python; familiarity with PyTorch, TensorFlow. Those are more like requirements. Preferred Qualifications: experience with CNNs, Vision Transformers, segmentation models; Modern C++ software development; knowledge of image enhancement, registration, feature extraction, anomaly detection; experience with large-scale datasets and quantitative algorithm evaluation. Thus we can have Requirements section (maybe combine required and minimum?). But we have separate headings: Requirements, Qualifications. Could treat Requirements as required qualifications, Qualifications as preferred qualifications. Or we could combine. We need to avoid filler like "commensurate with experience". Not present. We need to output only HTML fragment, no /, no markdown fences. Let's craft:

About the role

... intro paragraph describing role, division, etc.

Responsibilities

• ... each responsibility

Requirements

• ... required qualifications

Qualifications

• ... preferred qualifications

Skills

• ... maybe list of technical skills: Machine Learning, Deep Learning, Computer Vision, Image Processing, C++, Python, PyTorch, TensorFlow, Modern C++ software development, etc.

Benefits

• ... benefits list

Pay

Base Pay Range: $58.00 - $58.00 per hour based on pursuit of a Ph.D.

We should not include Schedule. We need to ensure we preserve all substantive info: also mention location: Primary Location: USA-CA-Milpitas-KLA. Could be included in About the role or maybe as a separate line. Could add a

location. Also mention Group/Division: The Semiconductor Products and Customers (Semi PC) business unit ... and RAPID division. That's substantive. Include in About the role. Also mention what you'll gain: hands-on experience etc. Could be included in About the role or maybe a separate section "What You'll Gain". But not in headings list; we could incorporate into About the role or Benefits? Probably better to add a section "What You'll Gain" but not in allowed headings list. The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). It gives examples but not exclusive? It says "use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule)." Likely they expect only those headings. So we should not add other headings. So we need to fit "What You'll Gain" content somewhere else, maybe under Benefits or About the role. Could add a paragraph under About the role describing what you'll gain. Similarly "About KLA" description can be part of About the role or maybe a separate paragraph. We need to preserve all substantive info, so we need to include that info somewhere. Let's craft About the role paragraph(s) covering: role description, division, what you'll do, what you'll gain, about KLA. We'll have multiple

under About the role. Now Responsibilities list: each bullet from "What You'll Do" and "Responsibilities". The posting says: "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." Then bullet list: Apply Machine Learning and Deep Learning techniques to image transformation and defect detection. Analyze large-scale image datasets and evaluate algorithm performance. Prototype algorithms using Python, PyTorch, and TensorFlow. Develop production-quality software using Modern C++ and Python. Apply best practices in software design, testing, debugging, code reviews, and version control. Collaborate with algorithm, software, systems, and application engineers to deploy solutions into production. We'll list those. Requirements: Required Qualifications: Strong background in Computer Vision, Image Processing, Machine Learning, Deep Learning, or Pattern Recognition. Strong programming skills in C++ and/or Python. Familiarity with PyTorch, TensorFlow, or equivalent ML frameworks. Also Minimum Qualifications: Pursuing a Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, Physics, Data Science, or a related field. We could combine both into Requirements. Qualifications (Preferred): Experience with CNNs, Vision Transformers, or segmentation models. Experience with Modern C++ software development. Knowledge of image enhancement, image registration, feature extraction, or anomaly detection. Experience with large-scale datasets and quantitative algorithm evaluation. Skills: maybe list of specific technical skills: Machine Learning, Deep Learning, Computer Vision, Image Processing, Pattern Recognition, C++, Python, PyTorch, TensorFlow, CNNs, Vision Transformers, segmentation models, Modern C++ software development, image enhancement, image registration, feature extraction, anomaly detection, large-scale datasets, quantitative algorithm evaluation. But that may be redundant with qualifications. However we can still include. Benefits list: 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. Pay: Base Pay Range: $58.00 - $58.00 per hour based on pursuit of a Ph.D. Location: Primary Location: USA-CA-Milpitas-KLA. Could be a

under About the role. Now ensure we don't include boilerplate: equal opportunity statement, use of AI statement, etc. Remove. Now produce HTML. Let's craft:

About the role

In this role you will play a key part in advancing business priorities by delivering high-impact work across your area of expertise within KLA’s Semiconductor Products and Customers (Semi PC) business unit, which designs, builds and sells KLA’s product portfolio to help chip manufacturers meet high-quality standards for technologies like AI, data centers, automotive and electronic devices. The unit’s inspection, metrology, and analytics systems detect and monitor defects across logic and memory chips, specialty semiconductors, wafers, reticles, advanced packaging, process equipment and materials, and also develop technologies for specialty processes, IC substrates, and PCB manufacturing, including etch and deposition, component inspection, and imaging and analytics. The RAPID division leads the industry in reticle inspection solutions, helping ensure high lithography yields and cost-effective semiconductor manufacturing.

You will gain hands‑on experience in AI, Computer Vision, Deep Learning, Image Processing, Modern C++, and Software Engineering; end‑to‑end exposure to algorithm development, optimization, and deployment; experience building scalable, production‑quality software; and mentorship from experts in algorithms, imaging, software engineering, and semiconductor inspection.

Primary Location: USA‑CA‑Milpitas‑KLA.

Responsibilities

• Apply Machine Learning and Deep Learning techniques to image transformation and defect detection.
• Analyze large‑scale image datasets and evaluate algorithm performance.
• Prototype algorithms using Python, PyTorch, and TensorFlow.
• Develop production‑quality software using Modern C++ and Python.
• Apply best practices in software design, testing, debugging, code reviews, and version control.
• Collaborate with algorithm, software, systems, and application engineers to deploy solutions into production.

Requirements

• Strong background in Computer Vision, Image Processing, Machine Learning, Deep Learning, or Pattern Recognition.
• Strong programming skills in C++ and/or Python.
• Familiarity with PyTorch, TensorFlow, or equivalent ML frameworks.
• Pursuing a Ph.D. in Computer Science, Computer Engineering, Electrical Engineering, Applied Mathematics, Physics, Data Science, or a related field.

Qualifications

• Experience with CNNs, Vision Transformers, or segmentation models.
• Experience with Modern C++ software development.
• Knowledge of image enhancement, image registration, feature extraction, or anomaly detection.
• Experience with large‑scale datasets and quantitative algorithm evaluation.

Skills