As a passionate AI Research Scientist - Algorithm at Applied Materials, you are part of a cross-functional team dedicated to applying cutting-edge AI and machine learning to accelerate scientific and materials innovation. Your mission involves creating domain-specific, product-centric algorithmic solutions that make a real impact for customers. In a collaborative environment that fosters out-of-the-box thinking and values diverse perspectives, you will have the opportunity to push boundaries and drive groundbreaking ideas through the synergy of technical expertise and teamwork.

• *Key Responsibilities:**
• Develop, pretrain, fine-tune, and align LLMs and generative models tailored for scientific and materials science data, literature, and workflows.
• Innovate post-training methods, alignment, and evaluation for domain-specific LLMs to ensure robust, accurate, and trustworthy models for scientific use cases.
• Design and implement generative approaches to accelerate materials discovery, hypothesis generation, and hardware design.
• Collaborate with scientists, engineers, and cross-functional teams to identify impactful applications of generative AI in materials science.
• Build and curate scientific datasets, benchmarks, and evaluation protocols for model validation and continuous improvement.
• Stay updated with advances in AI, machine learning, and materials science, and publish original research in top venues.
• Mentor junior team members and contribute to a collaborative, inclusive research culture.
• *Technical Skills:**
• Strong background in machine learning, deep learning, NLP, and generative AI, focusing on scientific or technical domains.
• Hands-on experience with LLM pretraining, supervised fine-tuning (SFT), post-training alignment (e.g., RLHF), and rigorous model evaluation.
• Proficiency in Python and frameworks like PyTorch or TensorFlow.
• Experience working with structured and unstructured scientific data (e.g., literature, experimental results, simulation outputs) and developing domain-specific models.
• Excellent communication skills to collaborate across disciplines and present complex ideas to diverse audiences.
• *Requirements/Education:**
• MS or Ph.D. degree in Computer Science, Computer Engineer, Electrical Engineer, Mathematics, Statistics, or related field.

By joining Applied Materials, you will be part of a supportive work culture that encourages learning, development, and career growth while tackling challenges and driving cutting-edge solutions for customers. You will have the opportunity to push boundaries, innovate, and contribute to shaping the future of science with AI. As a passionate AI Research Scientist - Algorithm at Applied Materials, you are part of a cross-functional team dedicated to applying cutting-edge AI and machine learning to accelerate scientific and materials innovation. Your mission involves creating domain-specific, product-centric algorithmic solutions that make a real impact for customers. In a collaborative environment that fosters out-of-the-box thinking and values diverse perspectives, you will have the opportunity to push boundaries and drive groundbreaking ideas through the synergy of technical expertise and teamwork.

• *Key Responsibilities:**
• Develop, pretrain, fine-tune, and align LLMs and generative models tailored for scientific and materials science data, literature, and workflows.
• Innovate post-training methods, alignment, and evaluation for domain-specific LLMs to ensure robust, accurate, and trustworthy models for scientific use cases.
• Design and implement generative approaches to accelerate materials discovery, hypothesis generation, and hardware design.
• Collaborate with scientists, engineers, and cross-functional teams to identify impactful applications of generative AI in materials science.
• Build and curate scientific datasets, benchmarks, and evaluation protocols for model validation and continuous improvement.
• Stay updated with advances in AI, machine learning, and materials science, and publish original research in top venues.
• Mentor junior team members and contribute to a collaborative, inclusive research culture.
• *Technical Skills:**
• Strong background in machine learning, deep learning, NLP, and generative AI, focusing on scientific or technical domains.
• Hands-on experience with LLM pretraining, supervised fine-tuning (SFT), post-training alignment (e.g., RLHF), and rigorous model evaluation.
• Proficiency in Python and frameworks like PyTorch or TensorFlow.
• Experience working with structured and unstructured scientific data (e.g., literature, experimental results, simulation outputs) and developing domain-specific models.
• Excellent communication skills to collaborate across disciplines and present complex ideas to diverse audiences.
• *Requirements/Education:**
• MS or Ph.D. degree in Computer Science, Computer Engineer, Electrical Engineer, Mathematics, Statistics, or related field.

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