Research Engineer Physics - PHD

Recruiting for an SF-based deep-tech startup building foundation ML models for multi-physics simulation. It’s a small, highly technical team — including multiple professors from top colleges like Berkeley, multiple PhDs, and even a Nobel Prize winner. Well funded, hybrid environment — actively hiring both researchers and engineers who want real ownership over foundational work and system-level impact.

By developing fast, generalizable, and accurate solvers, we aim to make inverse design and design space exploration accessible and ubiquitous — unlocking orders-of-magnitude improvements in how physical systems are engineered. Our focus is on generalizable, production-grade foundation models, not one-off surrogates.

We are seeking a Research Engineer (Physics PhD) to help build and scale next-generation physics simulation foundation models. This role sits at the intersection of scientific machine learning, computational physics, and large-scale AI systems.

You will contribute both to advancing core simulation methodologies and to translating cutting‑edge research into scalable, production-ready systems. This is a hands‑on role for someone who is equally comfortable deriving equations, implementing numerical methods, and working with modern ML infrastructure.

You will collaborate closely with ML researchers, HPC engineers, and domain experts to push the frontier of physics‑informed AI systems.

• Advance research in scientific machine learning for multi‑physics systems
• Develop physics‑informed and physics‑constrained learning approaches
• Contribute to the design of foundation models tailored to structured physical systems
• Explore novel architectures that combine numerical solvers with neural operators or generative systems

• Work on wave physics and numerical simulation methods (FDTD, FDFD, FEM)
• Implement and optimize large‑scale simulation pipelines
• Validate model outputs against analytical solutions and high‑fidelity solvers
• Improve numerical stability, convergence, and computational efficiency
• Train and scale AI models on HPC infrastructure
• Work with distributed training systems and GPU clusters
• Design datasets and evaluation frameworks for simulation‑grounded ML systems
• Analyze performance tradeoffs between simulation fidelity and model generalization

• Collaborate with interdisciplinary research and engineering teams
• Translate research prototypes into production‑grade implementations
• Use Python for simulation, modeling, experimentation, and data analysis
• Contribute to internal tooling that supports reproducible scientific experimentation

• PhD (or equivalent experience) in Physics, Applied Mathematics, Computational Science, or a closely related field
• Strong background in numerical simulation methods (e.g., FDTD, FDFD, FEM)
• Experience in scientific machine learning or physics‑informed ML
• Proficiency in Python and scientific computing ecosystems (Num Py, Sci Py, PyTorch, JAX, etc.)
• Experience working with large‑scale simulations and HPC environments

• Experience with wave physics, electromagnetics, or related domains
• Familiarity with neural operators, operator learning, or surrogate modeling approaches
• Experience training large ML models at scale
• Comfort working across research and engineering boundaries
• Prior experience in early‑stage or research‑intensive environments

• New scientific ML approaches meaningfully improve simulation speed and generalization
• Numerical methods are both theoretically sound and practically scalable
• Research translates efficiently into deployed systems
• Simulation and AI models are tightly integrated rather than siloed
• The company’s foundation models advance the state of the art in physics‑based AI

• Work at the frontier of scientific AI and computational physics
• Contribute to foundational research with real industrial impact
• High ownership and intellectual autonomy
• Collaborate with world‑class scientists and engineers
• Help define a new paradigm for engineering design and simulation