Multiphysics Simulation Scientist, Semiconductors

External

Periodic-labs · Menlo Park

Full-timeOn-site4w ago

Python

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About the role

Periodic Labs is developing AI systems that can simulate physical science, verify predictions, and train on the full scientific method. A core part of that mission is building high-fidelity computational models of the processes happening inside our experimental and customer-facing systems, then making those models fast enough to support AI planning, autonomous lab operation, and engineering decision-making. We are looking for a Multiphysics Simulation Scientist to develop, validate, accelerate, and integrate models of semiconductor-relevant physical processes. This role is designed for someone with deep experience in multiphysics simulation, especially in domains such as advanced packaging, wafer mechanics, thin-film deposition, plasma processing, thermal transport, stress and warpage, capillary flow, electromagnetics, or related manufacturing processes. You will work across computational science, AI, automation, process engineering, and customer-facing teams. Your models will help us understand experimental systems, generate training data, guide process decisions, and deliver high-value engineering insights to semiconductor customers.

Responsibilities

Build and apply multiphysics models for semiconductor-relevant systems, including thermal, mechanical, fluid, electromagnetic, plasma, chemical reaction, and materials processes, often in coupled settings.

Use high-fidelity simulation tools such as COMSOL, ANSYS, Abaqus, Fluent, Lumerical, Sentaurus, OpenFOAM, MOOSE, or comparable platforms where appropriate, while also helping decide when custom solvers, reduced-order models, or surrogate models are needed.

Validate models against experimental and process data. You will work with experimentalists and engineers to compare simulations against measurements, estimate uncertain parameters, understand failure modes, and decide when a model is ready to guide real decisions.

Generate physically meaningful simulated datasets for ML training. Your simulations will help train AI systems in regimes where experiments are expensive, slow, or difficult to run.

Integrate simulation workflows with Periodic Labs' AI, data, and orchestration infrastructure. Your models should become callable tools for AI planning and experiment interpretation, not standalone reports.

Collaborate with process, automation, AI, facilities, and customer-facing teams to optimize R&D workflows and solve practical engineering problems.

Help define the long-term multiphysics modeling roadmap for Periodic Labs' semiconductor and materials programs.

You Will Thrive in This Role If You Have

A PhD, MS, or equivalent experience in mechanical engineering, chemical engineering, materials science, electrical engineering, applied physics, aerospace engineering, or a closely related discipline.

Significant hands-on experience with multiphysics modeling tools such as COMSOL, ANSYS, Abaqus, Fluent, OpenFOAM, Sentaurus, Lumerical, MOOSE, or other finite-element, finite-volume, particle, or continuum solvers.

Deep understanding of coupled physical processes relevant to semiconductor or advanced manufacturing systems, such as heat transfer, stress and deformation, capillary flow, diffusion, plasma dynamics, electromagnetics, surface reactions, phase change, deposition, or materials evolution.

Experience building simulations that influenced real engineering or scientific decisions. You have not only published or run models; you have used them to explain failures, guide designs, improve processes, or support customer deliverables.

Strong Python skills and the ability to connect simulation outputs to analysis workflows, data pipelines, ML training infrastructure, and downstream decision-making systems.

Comfort working across disciplines with process engineers, experimental scientists, ML researchers, automation engineers, and external technical stakeholders.

Good judgment about simulation fidelity. You know when a commercial multiphysics package is the right answer, when a custom model is needed, and when a fast approximation is more useful than a slow high-fidelity model.

Especially Strong Candidates May Also Have

Deep knowledge

Multiphysics Simulation Scientist, Semiconductors

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