AI Algorithm - Intern at Applied Materials in Santa Clara,CA

Summary: Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. They are seeking an AI Algorithm Intern to assist in developing, training, and evaluating large language models and generative AI models applied to scientific and materials science data. Responsibilities: - Assist in developing, training, and evaluating large language models (LLMs) and generative AI models applied to scientific and materials science data - Support experimentation with fine-tuning, alignment techniques, and evaluation methods for domain-specific AI models under the guidance of senior researchers - Help collect, clean, and curate scientific datasets (e.g., literature, experimental data, simulations) and contribute to benchmarks and evaluation pipelines - Document experiments, results, and learnings clearly; contribute to internal reports or research prototypes Required Qualifications: - MS or Ph.D. degree in Computer Science, Computer Engineer, Electrical Engineer, Mathematics, Statistics or related field Preferred Qualifications: - Coursework or research experience in machine learning, deep learning, NLP, or generative AI - Familiarity with Python and at least one deep learning framework such as PyTorch or TensorFlow - Exposure to concepts such as model fine-tuning, evaluation, or prompt engineering; prior hands-on LLM experience is a plus but not required - Interest in working with scientific or technical data (e.g., research papers, experimental datasets, simulations) - Strong problem-solving skills and curiosity, with the ability to learn quickly in a research-focused environment - Good communication skills and willingness to collaborate across disciplines Required Skills: Machine learning, Deep learning, Natural language processing, Generative AI, Python, PyTorch, TensorFlow, Model fine-tuning, Scientific data analysis, Research Benefits: Comprehensive benefits package, Participation in a bonus and a stock award program