AI Materials Research Engineer at Applied Materials (Santa Clara, USA)
Job Description
Applied Materials is hiring an AI materials research engineer in Santa Clara to speed up semiconductor materials discovery by combining machine learning with computational materials science (requisition R2626716).
At a glance
- Position: AI Materials Research Engineer
- Where: Santa Clara, California, USA
- Pay: US$170,000–234,000 a year (posted range)
- Type: Full time, regular
- Experience: MS or PhD plus 2–5 years
- Apply: open until filled (re-checked 17 October 2026)
About the AI materials research engineer role
- Build machine-learning models for materials property prediction, screening, optimisation, process-performance modelling and generative materials design
- Apply computational methods including density functional theory (DFT), molecular dynamics (MD), kinetic Monte Carlo and phase-field simulation
- Train AI surrogate models that stand in for expensive simulations
- Create materials-informatics pipelines that bring together experimental data, characterisation results, simulation outputs and the scientific literature
- Develop AI copilots and agent-based workflows for literature review, hypothesis generation, experiment planning and running simulations
- Work with materials scientists, process engineers and AI teams to deliver scientific AI tools
Why this role matters
New transistor and memory generations depend on materials that behave well at a few atoms’ thickness, and testing each candidate in a fab is slow and costly. This team sits at the start of that pipeline, using simulation and machine learning to narrow the search before any wafer is processed. It suits a computational materials scientist who wants their models to shape real manufacturing decisions, not just papers.
What Applied Materials is looking for
- An MS or PhD in materials science, computational materials science, physics, chemical engineering or a related field
- Two to five years of experience in computational materials science, materials informatics, scientific machine learning or AI for science
- Strong Python and machine-learning skills with PyTorch, TensorFlow or scikit-learn
- Hands-on experience with at least one of DFT, MD, kinetic Monte Carlo or phase-field modelling
- A solid grasp of crystal structures, thermodynamics, kinetics, defect physics and semiconductor materials
Nice to have
- Simulation codes such as VASP, Quantum ESPRESSO, CP2K, LAMMPS or GROMACS
- Materials databases such as the Materials Project, OQMD or NOMAD
- Graph neural networks, materials foundation models, physics-informed ML or generative models for materials
- Large-scale training and simulation on cloud or HPC systems
Pay and location
The posting gives a salary range of US$170,000 to US$234,000 a year for Santa Clara; the offer depends on location, grade, skills and experience, and bonus or stock awards may apply. The posting says the role is not 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 17 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
- Bring one end-to-end example: a project where simulation data trained a model that then guided a real materials choice.
- Know the surrogate trade-off: be ready to discuss when a machine-learned interatomic potential can replace DFT, and how you check its errors.
- Speak semiconductor: revise high-k dielectrics, barrier and liner metals, and defect-driven reliability issues.
- Agents with care: if you mention LLM agents for literature or experiment planning, explain how you would validate their output.
- Data hygiene: prepare to explain how you merge experimental and simulated data with different noise levels.
About Applied Materials
Applied Materials is a US company, headquartered in Santa Clara, California, that makes the equipment chipmakers use to deposit, etch, implant, inspect and measure materials on silicon wafers, along with related software and services. It is one of the largest suppliers of semiconductor manufacturing equipment in the world, and its customers include the major logic, memory and foundry manufacturers. It also has large engineering operations in India, Singapore, Israel and Europe.
