AI Research Scientist (Generative Models for Scientific Discovery)

Applied Materials • Santa Clara, CA

13d

About The Position

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, ensuring models are robust, accurate, and trustworthy 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 current 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.

Requirements

• Strong background in machine learning, deep learning, NLP, and generative AI, with a focus 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 such as 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, with the ability to collaborate across disciplines and present complex ideas to diverse audiences.
• MS or Ph.D. degree in Computer Science, Computer Engineer, Electrical Engineer, Mathematics, Statistics or related field

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, ensuring models are robust, accurate, and trustworthy 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 current 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.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 5,001-10,000 employees