AI Research Scientist - Algorithm

Who We Are

Applied Materials is a global leader in materials engineering solutions used to produce virtually every new chip and advanced display in the world. We design, build and service cutting-edge equipment that helps our customers manufacture display and semiconductor chips - the brains of devices we use every day. As the foundation of the global electronics industry, Applied enables the exciting technologies that literally connect our world - like AI and IoT. If you want to push the boundaries of materials science and engineering to create next generation technology, join us to deliver material innovation that changes the world.

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AI Research Scientist - Algorithm

We are a passionate, cross-functional team at the forefront of applying cutting-edge AI and machine learning to accelerate scientific and materials innovation. Our mission is to create domain-specific, product-centric algorithmic solutions that drive real impact for our customers.

We thrive in a collaborative environment that encourages out-of-the-box thinking and values diverse perspectives. Here, creativity flourishes-groundbreaking ideas are born from the synergy of technical expertise and open-minded teamwork. We believe the best solutions emerge when everyone is empowered to share their unique insights and challenge conventional boundaries.

Our team leverages state-of-the-art generative AI and large language models to tackle complex problems in materials science, scientific discovery, and hardware design. We work closely with scientists, engineers, and product leaders to translate frontier research into practical, high-value applications.

Ideal candidates bring a strong research background, technical leadership, and a passion for learning new technologies. If you are excited to solve complex problems, drive innovation, and help shape the future of science with AI, join us on our journey to make possible a better future through intelligent discovery.

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, 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.

TECHNICAL SKILLS:

• 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.

REQUIREMENTS/EDUCATION:

MS or Ph.D. degree in Computer Science, Computer Engineer, Electrical Engineer, Mathematics, Statistics or related field

Additional Information

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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.