Principal AI Data Scientist – Scientific AI & Physics-Informed Machine Learning

Key Responsibilities

• Develop and deploy advanced AI/ML solutions for semiconductor process and device simulations, Electronic Design Automation (EDA), packaging, reliability, and manufacturing applications.

Create physics-informed and hybrid AI models that integrate experimental data, simulation outputs, and domain knowledge.

• Build surrogate models and scientific machine learning frameworks to accelerate computationally intensive simulations and engineering workflows.

• Research and apply state-of-the-art techniques including deep learning, generative AI, graph neural networks, neural operators, and foundation models.

• Collaborate with semiconductor experts, software engineers, and product teams to transition research into production solutions.

• Drive innovation through patents, publications, and technical leadership across STM and Applied Materials.

• Effective technical verbal/written communication representing the org with limited supervision. Ability to collaborate with internal stakeholders, customers and vendors.
• Able to follow complex program schedules, budgets, and milestones with limited supervision.
• Collaborates/participate in discussions to solve interdisciplinary technical issues in a cross-functional team environment.

Required Qualifications

• PhD in Electrical Engineering, Physics, Materials Science, Computer Science, Applied Mathematics, Computational Science, or related discipline.

• Strong expertise in machine learning, deep learning, statistical modeling, and scientific computing.

• Hands-on experience with Python and modern AI frameworks such as PyTorch, TensorFlow, or JAX.

• Strong background in numerical methods, optimization, simulation, or computational modeling.

• Excellent communication skills and ability to work across multidisciplinary teams.

Preferred Qualifications

• Semiconductor industry experience in process, device, reliability, metrology, packaging, EDA, or manufacturing.

• 5+ years of experience developing advanced AI/ML algorithms for scientific or engineering applications.

• Experience with Physics-Informed Neural Networks (PINNs), neural operators, surrogate modeling, uncertainty quantification, or digital twins.

• Familiarity with TCAD, FEM, CFD, Monte Carlo, multiphysics simulation, or scientific computing environments.

• Experience with foundation models, generative AI, multimodal learning, or graph neural networks.

• Strong publication and/or patent record demonstrating technical innovation and thought leadership. The background we're targeting is similar to senior researchers who combine semiconductor device physics, computational modeling, and advanced AI research

## Qualifications

### Education:

Master's Degree

### Skills

### Certifications:

### Languages:

### Years of Experience:

7 - 10 Years

### Work Experience:

## Additional Information

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### Shift:

10-Day 8-Hr (United States of America)

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### Travel:

Yes, 10% of the Time

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### Relocation Eligible:

Yes

### Referral Payment Plan:

Employee Referral (Standard)

Salary Range:

$0.00 - $0.00

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 committed to diversity in the workplace. All 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.