Algorithm Developer

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

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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. 

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Algorithm Developer 

Education: Mtech/Phd 

Experience: NCG 

Role Summary 

Join the DefectExpert program to build and enhance an AI-powered defect recommendation engine for semiconductor tools. In this role, you will contribute to deep learning, NLP, computer vision, and large language model based solutions that strengthen the Defect Knowledge Base (KB) and enable intelligent recommendation workflows for field engineers diagnosing and resolving defect-related issues. 

This role is suitable for early-career AI/ML engineers or algorithm developers experience in LLMs, computer vision, image analysis, text understanding, and production-oriented algorithm development. 

Responsibilities 

• Analyse defect descriptions, field reports, logs, and tool guides using NLP/LLMs to help build the Defect Knowledge Base (KB). 

• Develop and optimize methods to automatically extract, clean, cluster, and summarize defect information from structured and unstructured sources. 

• Improve the accuracy of the recommendation engine that maps defect symptoms to probable causes using classical ML and LLM‑based reasoning. 

• Train and experiment with deep learning models for NLP and computer vision problem statements, including creating embeddings, classification, retrieval, and similarity models. 

• Design, evaluate, and integrate LLM workflows such as prompting, retrieval augmentation, evaluation, fine-tuning, and structured/unstructured data integration. 

• Work with wafer maps, defect logs, and visual patterns to extract meaningful signals. 

• Build reliable utilities, pipelines, and tools for data processing, model evaluation, workflow automation, and visualization. 

• Document technical findings, experiments, assumptions, and insights clearly, and present progress, risks, and recommendations in regular team reviews. 

Required Skills 

• Strong hands-on Python programming experience, including writing clean, modular, and maintainable code. 

• Working knowledge of deep learning frameworks such as PyTorch or TensorFlow, with experience training, evaluating, or deploying models. 

• Hands-on experience with NLP and/or computer vision techniques such as embeddings, CNNs/Transformers, text preprocessing, image preprocessing, OpenCV, or related libraries. 

• Practical understanding of LLMs, including inference, prompt design, evaluation, and integration with application workflows. 

• Familiarity with traditional ML concepts such as feature engineering, ensemble methods (bagging/boosting), and train/validation workflows. 

• Experience working with Databricks, ETL/data pipelines, and at least one database query language such as SQL. 

• Strong analytical, debugging, and problem-solving mindset with the ability to independently investigate technical issues. 

• Exposure with LLM fine‑tuning (QLoRA, PEFT). 

• Exposure to agentic frameworks like Langchain, Langgraph.   

• Exposure to prompt engineering and evaluation methods. 

• Understanding of vector databases, embeddings, or semantic search. 

• Familiarity with YOLO/Mask R‑CNN or transformer‑based vision models. 

Nice to Have 

• Basic semiconductor domain knowledge (process steps, chambers, defects). 

• Experience with object detection, image similarity, or retrieval systems. 

• Experience contributing to applied ML, computer vision, NLP, or data science projects in academic, industry, or open-source settings. 

• GitHub, Kaggle, publication, or project portfolio links are encouraged where available. 

Additional Information

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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.