Research Engineer - Computational Physics
Listed on 2026-01-22
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Engineering
Systems Engineer, Research Scientist, Robotics, Artificial Intelligence
We are a Berkeley-based startup pioneering a new era of physics-driven design and simulation. Our mission is to transform how the physical world is modeled, optimized, and engineered—across industries including telecommunications, semiconductors, aerospace, construction, consumer electronics, and healthcare.
We are developing foundation models for physics simulations—generalizable, high-fidelity solvers that accelerate inverse design and enable scalable design space exploration. Following the successful deployment of our proof-of-concept model, we are now scaling our infrastructure, broadening the scope of our simulations, and expanding our research and engineering team.
We're seeking exceptional researchers and engineers to help advance the frontier of scientific machine learning and computational physics.
Desired Qualifications
- PhD in Physics, Applied Mathematics, Computational Science, and relevant fields.
- Proven contributions to computational physics and scientific machine learning (through publications or industrial experience).
- Strong background in wave physics and numerical simulation methods (e.g., FDTD, FDFD, FEM), ideally in electromagnetics.
- Experience training AI models and running large-scale simulations on high-performance computing systems.
- Familiarity with parallelization strategies, performance optimization, and distributed training pipelines.
- Hands-on experience with commercial (e.g., Lumerical, COMSOL, HFSS, CST) or open-source (e.g., fdtd-z, MEEP, Devito) wave solvers.
- Proficiency in Python for simulation, modeling, and data analysis.
- Strong communication and collaboration skills in interdisciplinary teams.
This is an opportunity to contribute to cutting-edge research and development in a dynamic and collaborative startup environment. If you're passionate about pushing the boundaries of physics simulation, we’d love to hear from you!
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