Company Overview
Group/Division
Job Description/Preferred Qualifications
Role
You will be part of a cutting-edge team working on Large Language Models (LLMs), Machine Learning, Deep Learning, and Retrieval-Augmented Generation (RAG) pipelines. You’ll help design, build, and evaluate AI systems that solve complex real-world problems at scale.
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Key Responsibilities
• Develop and optimize RAG pipelines: document chunking, embedding generation, vector storage, retrieval, reranking, and grounded generation with citations.
• Work on LLM-based applications: fine-tuning open-source models (LLaMA, Mistral, etc.), building prompt strategies, and deploying inference services.
• Contribute to machine learning models (classification, regression, recommendation, anomaly detection) and deep learning architectures (CNNs, RNNs, Transformers).
• Implement robust model evaluation frameworks (accuracy, F1, BLEU, perplexity, hallucination detection, relevance).
• Collaborate with senior engineers on scalable pipelines, guardrails, and integration with enterprise systems.
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Required Skills
• Programming & Foundations
• Strong in Python, data structures, and algorithms.
• Hands-on with NumPy, Pandas, Scikit-learn for ML prototyping.
• Machine Learning
• Understanding of supervised/unsupervised learning, regularization, feature engineering, model selection, cross-validation, ensemble methods (XGBoost, LightGBM).
• Deep Learning
• Proficiency with PyTorch (preferred) or TensorFlow/Keras.
• Knowledge of CNNs, RNNs, LSTMs, Transformers, Attention mechanisms.
• Familiarity with optimization (Adam, SGD), dropout, batch norm.
• LLMs & RAG
• Hugging Face Transformers (tokenizers, embeddings, model fine-tuning).
• Vector databases (Milvus, FAISS, Pinecone, ElasticSearch).
• Prompt engineering, function/tool calling, JSON schema outputs.
• Data & Tools
• SQL fundamentals; exposure to data wrangling and pipelines.
• Git/GitHub, Jupyter, basic Docker.
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Nice to Have
• Built a personal ML/LLM project (chatbot, RAG app, document Q&A, finetuned model).
• Familiarity with LangChain/LlamaIndex/Agno frameworks.
• Knowledge of cloud platforms (Azure/AWS/GCP) and MLOps basics (CI/CD, MLflow, W&B).
• Exposure to knowledge graphs or multi-agent workflows.
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What We’re Looking For
• Solid academic foundation with strong applied ML/DL exposure.
• Curiosity to learn cutting-edge AI and willingness to experiment.
• Clear communicator who can explain ML/LLM trade-offs simply.
• Strong problem-solving and ownership mindset.
Minimum Qualifications
Doctorate (Academic) Degree and 0 years related work experience; Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years
We offer a competitive, family friendly total rewards package. We design our programs to reflect our commitment to an inclusive environment, while ensuring we provide benefits that meet the diverse needs of our employees.
KLA is proud to be an equal opportunity employer
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.
Skills Required
- Doctorate Degree and 0 years related work experience; or Master’s Degree with 3 years experience; or Bachelor’s Degree with 5 years experience
KLA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about KLA and has not been reviewed or approved by KLA.
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Retirement Support — Retirement offerings include a 401(k) plan with company matching and financial planning support. Student debt assistance and related financial benefits reinforce long-term savings and security.
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Equity Value & Accessibility — Ownership programs include an Employee Stock Purchase Plan and broad-based RSU participation that extend equity beyond a narrow group. These elements complement competitive pay and bonuses to strengthen total rewards.
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Leave & Time Off Breadth — Time-off programs span paid time off, paid company holidays, and paid volunteer time. Family care and bonding leave and back-up care services add flexibility during life events.
KLA Insights
What We Do
KLA develops industry-leading equipment and services that enable innovation throughout the electronics industry. We provide advanced process control and process-enabling solutions for manufacturing wafers and reticles. In close collaboration with leading customers across the globe, our expert teams of physicists, engineers, data scientists and problem-solvers design solutions that move the world forward.








