AI Research Scientist - Algorithm

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.

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.

TECHNICAL SKILLS:

• Strong background in machine learning, deep learning, NLP, and generative AI, with a focus on scientific or technical domains.

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.

Relocation Eligible

• Applied Materials is an Equal Opportunity Employer. Qualified applicants will receive consideration for employment without regard to race, color, national origin, citizenship, ancestry, religion, creed, sex, sexual orientation, gender identity, age, disability, veteran or military status, or any other basis prohibited by law.