Physics AI Scientist at Applied Materials (Bangalore, India)
Job Description
This scientific machine learning job in Bangalore is a Physics AI Scientist role at Applied Materials, the semiconductor-equipment company, building AI models that combine physics-based simulation with machine learning to speed up simulation and engineering design for semiconductor manufacturing (requisition R2625974).
At a glance
- Position: Physics AI Scientist (full time, regular)
- Where: Bangalore, India; about 10% travel; relocation eligible
- Pay: Not stated in the posting
- Apply: open until filled (re-checked 23 October 2026)
What the scientific machine learning role involves
- Develop scientific AI models that bridge scientific computing, physics-based simulation and machine learning, for engineering problems rooted in physics and chemistry
- Design and train surrogate models, operator-learning models and foundation models for scientific simulations
- Work with domain experts to frame AI solutions for complex engineering problems
- Build scalable workflows for data generation, training, validation and deployment of these models
- Publish technical innovations and drive the adoption of scientific AI across engineering teams
Why this role matters
Chip-equipment design leans on slow multiphysics simulations of plasmas, heat transfer, fluid flow and chemistry. A surrogate that answers in seconds instead of hours changes how many designs engineers can try. This role sits in the part of industrial AI that needs real physics knowledge rather than generic model training. The posting also expects publications, which is uncommon in industry in India.
What Applied Materials is looking for
- The posting lists no separate minimum qualifications; everything it asks for is under preferred qualifications, below
Nice to have
- Ph.D. in Mechanical Engineering, Chemical Engineering, Materials Science, Physics, Applied Mathematics, Computer Science or a related field
- A strong background in scientific machine learning and numerical simulation
- Experience with surrogate modelling, physics-informed neural networks (PINNs), operator learning such as Fourier neural operators, or foundation models
- Proficiency in Python and PyTorch
- Experience with HPC, distributed training, large scientific datasets or scalable ML workflows
Pay and location
Applied Materials does not state pay for this role. It is a full-time regular position in Bangalore, with travel about 10% of the time, and the posting says it is eligible for relocation.
How to apply
Apply through the official Applied Materials job posting. Applied Materials’s posting does not give a closing date, so the role is open until filled; ResearchJobs.in will re-check this listing on 23 October 2026. Check the posting for location and work-authorisation details before you apply.
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Hiring institution: Applied Materials
Official advertisement: amat.wd1.myworkdayjobs.com
How to prepare for this application
- Prepare one scientific-ML project you can defend in depth: the PDE or simulator behind it, how you generated the training data, and where the surrogate breaks down
- Be ready to compare PINNs with operator-learning methods such as FNO, and say when you would choose each
- Show PyTorch code that scales: distributed training, data loaders for large simulation outputs, and experiment tracking
- Learn the basics of the semiconductor processes the company's equipment runs (deposition, etch, plasma processing) so you can talk to domain experts in their terms
- Bring your publication list; the posting expects the scientist to publish
About Applied Materials
Applied Materials is a US-headquartered company that makes the equipment, services and software used to manufacture semiconductor chips and advanced displays, including deposition, etch, implant and inspection systems. It has large engineering and R&D operations in India, including in Bangalore and Chennai.
