Machine Learning Engineer, Generative AI

Applied Materials

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Quick summary

Work type: On-site
Location: Santa Clara, CA
Employment: Full-time
Posted: 127 days ago
Freshness: Confirmed live today
Nearby: 99+ roles within 25 mi

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How this pay compares to similar roles

Similar $219k

$152k $277k

below market most similar roles pay here above market

This listing doesn't post a salary. Most similar roles pay $184,613–$253,625.

Based on 240 similar postings.

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About Applied Materials

Applied Materials is the world''s largest supplier of equipment, services, and software for the semiconductor and display industries, enabling the production of chips and advanced displays. Industry: Semiconductor Equipment

Applied Materials currently has 337 open roles on FindRole.

Listed pay typically runs $124,000–$171,000 across 219 roles with salary data.

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At a glance

TL;DR · Machine Learning Engineer, Generative AI

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The Machine Learning Engineer (Generative AI) joins a cross-functional team focused on applying cutting-edge AI to accelerate scientific and materials innovation. This role involves developing, pretraining, fine-tuning, and aligning large language models and generative models specifically for scientific data, literature, and workflows. You will design generative approaches to accelerate materials discovery, hypothesis generation, and hardware design while building scientific datasets and evaluation protocols. The position requires proficiency in Python and frameworks like PyTorch or TensorFlow, alongside expertise in deep learning, NLP, and post-training alignment methods like RLHF. You will solve complex problems in materials science by translating frontier research into practical applications, working with structured and unstructured scientific data to ensure models are robust and accurate for technical use cases.

What does a Machine Learning Engineer earn in California?

Median $235750 from 200 postings across 34 companies.

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Skills

Generative AI Large Language Models Machine Learning Deep Learning NLP Python PyTorch TensorFlow RLHF Supervised Fine-Tuning SFT Model Evaluation

What you'll do

Develop, pretrain, fine-tune, and align LLMs and generative models for scientific and materials science data.
Innovate post-training methods, alignment, and evaluation to ensure models are robust and accurate for scientific use cases.
Design and implement generative approaches to accelerate materials discovery, hypothesis generation, and hardware design.
Build and curate scientific datasets, benchmarks, and evaluation protocols for model validation and continuous improvement.
Identify impactful applications of generative AI in materials science by working with scientists and engineers.
Stay current with AI and materials science advances and publish original research in top venues.
Mentor junior team members and contribute to a collaborative research culture.

What we're looking for

MS or Ph.D. degree in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, Statistics, or a related field.
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, post-training alignment, and rigorous model evaluation.
Proficiency in Python and frameworks such as PyTorch or TensorFlow.
Experience working with structured and unstructured scientific data and developing domain-specific models.
Excellent communication skills to collaborate across disciplines and present complex ideas to diverse audiences.
Ability to travel up to 10% of the time.