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Machine Learning Engineer (Generative AI)Applied Materials • United States
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Machine Learning Engineer (Generative AI)

Applied Materials
  • US
    United States
  • US
    United States
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Über

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.
What We Offer
Salary:
$131,000.00 - $180,000.00
Location:
Santa Clara,CA
You’ll benefit from a supportive work culture that encourages you to learn, develop, and grow your career as you take on challenges and drive innovative solutions for our customers. We empower our team to push the boundaries of what is possible—while learning every day in a supportive leading global company. Visit our Careers website to learn more.
At Applied Materials, we care about the health and wellbeing of our employees. We’re committed to providing programs and support that encourage personal and professional growth and care for you at work, at home, or wherever you may go. Learn more about our benefits (https://hrportal.ehr.com/applied/) .
TEAM OVERVIEW:
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
Time Type:
Full time
Employee Type:
New College Grad
Travel:
Yes, 10% of the Time
Relocation Eligible:
Yes
The salary offered to a selected candidate will be based on multiple factors including location, hire grade, job-related knowledge, skills, experience, and with consideration of internal equity of our current team members. In addition to a comprehensive benefits package, candidates may be eligible for other forms of compensation such as participation in a bonus and a stock award program, as applicable.
For all sales roles, the posted salary range is the Target Total Cash (TTC) range for the role, which is the sum of base salary and target bonus amount at 100% goal achievement.
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.
In addition, Applied endeavors to make our careers site (https://www.appliedmaterials.com/us/en/careers.html) accessible to all users. If you would like to contact us regarding accessibility of our website or need assistance completing the application process, please contact us via e-mail at Accommodations_Program@amat.com, or by calling our HR Direct Help Line at 877-612-7547, option 1, and following the prompts to speak to an HR Advisor. This contact is for accommodation requests only and cannot be used to inquire about the status of applications.
  • United States

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