PhD New Graduate Algorithm Developer in Physics-Informed Machine Learning at Applied Materials (Santa Clara, USA)
Job Description
Applied Materials is recruiting a PhD graduate algorithm developer in physics-informed machine learning for its Rocket team in Santa Clara, with a start between November 2026 and February 2027 preferred (requisition R2628587).
At a glance
- Position: New College Grad – Algorithm Developer III (PhD)
- Where: Santa Clara, California, USA
- Pay: US$161,000–221,000 a year (posted range)
- Type: Full time; about 10% travel; relocation eligible
- Start: November 2026 – February 2027 preferred
- Apply: 23 October 2026 (the posting says applications are reviewed on a rolling basis and the role may close early)
About the physics-informed machine learning role
- Design scalable pipelines that collect and analyse high-volume data from thousands of sensors on semiconductor manufacturing tools
- Identify and model the physical relationships that govern complex process behaviour
- Develop predictive and control algorithms that combine first-principles physics with machine learning, including physics-informed neural networks (PINNs)
- Build machine-learning surrogates and reduced-order models from rigorous physics simulations, balancing accuracy against speed
- Analyse sensor and metrology data to improve simulations and models iteratively
- Write maintainable software that runs on real manufacturing tools, control systems or dashboards
Rocket is a group inside Applied Materials that develops and scales rapid-innovation methods, combining scientific depth, computation and engineering to tackle hard problems in chip manufacturing.
Why this role matters
Many machine-learning jobs never touch physical equipment. This one puts models inside the control of tools that process real wafers, where a physics-aware model can cut drift, scrap and tuning time. It is an unusual entry point for a new PhD in physics, applied mathematics or computational science who wants to move into industry without giving up modelling.
What Applied Materials is looking for
- A PhD in physics, applied mathematics, computational chemistry, electrical or mechanical engineering, or a related quantitative field; or a master’s degree in one of these with at least 2 years of relevant industry or research experience
- Deep expertise in theoretical physics, applied mathematics, computational science or simulation-driven engineering
- Working knowledge of modern machine learning, data science and statistical modelling
- Strong software engineering: turning complex algorithms into clean, scalable, production-ready code
Nice to have
- Availability to start full time between November 2026 and February 2027
- Public code, open-source contributions or peer-reviewed publications that show code quality
- Experience with computer-aided engineering, engineering simulation or physical modelling
- Machine learning applied to scientific, engineering or industrial problems
- Interest in sensors, instrumentation, hardware or robotics
Pay and location
The posting gives a salary range of US$161,000 to US$221,000 a year for Santa Clara, depending on location, grade, skills and experience, with possible bonus and stock awards. The role involves about 10% travel and is eligible for relocation.
How to apply
Apply through the official Applied Materials page. The closing date is 23 October 2026 (the posting says applications are reviewed on a rolling basis and the role may close early).
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Hiring institution: Applied Materials
Official advertisement: amat.wd1.myworkdayjobs.com
How to prepare for this application
- Explain a PINN honestly: know when physics-informed losses help and when they make training harder, with an example from your own work.
- Reduced-order models: revise POD, Gaussian-process surrogates and neural operators, and how you would validate one against a full simulation.
- Sensor data at scale: be ready to design a pipeline for thousands of noisy, time-synchronised sensor channels.
- Show your code: the posting asks for evidence of code quality, so tidy up a public repository before applying.
- Apply early: applications are reviewed on a rolling basis and the role may close before the stated date.
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.
