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AI Research Scientist (Generative Models for Scientific Discovery) at Applied Materials
Santa Clara, California, United States -
Full Time

Start Date

Immediate

Expiry Date

10 Mar, 26

Salary

0.0

Posted On

10 Dec, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Machine Learning, Deep Learning, NLP, Generative AI, LLM Pretraining, Supervised Fine-Tuning, Post-Training Alignment, Model Evaluation, Python, PyTorch, TensorFlow, Scientific Data, Domain-Specific Models, Communication Skills, Collaboration, Mentoring

Industry

Semiconductor Manufacturing

Description
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. 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

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Responsibilities
Develop and fine-tune generative models for scientific discovery, focusing on materials science data and workflows. Collaborate with cross-functional teams to identify applications of generative AI and build scientific datasets for model validation.