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.

The work tackles hard physics problems (EM, acoustics, wave propagation) at the intersection of scientific computing, numerical PDEs, and frontier ML - including neural operators, PINNs, and transformer-based solvers. In parallel, we are actively exploring GenAI and LLM-based systems for physics workflows, from simulation orchestration and reasoning to hybrid ML-PDE pipelines.

Our focus is on generalizable, production-grade foundation models, not one-off surrogates.

Hiring multiple RESEARCH ENGINEER positions across - Computational Physics/Applied Mathematics/Generative AI

As a Research Engineer you will sit at the intersection of scientific machine learning, computational physics, and large-scale AI systems - helping build and scale next-generation simulation foundation models.

• Advance research and engineering in scientific machine learning and physics simulations
• Develop and fine-tune large-scale generative AI models
• Work on wave physics and numerical simulation methods (FDTD, FDFD, FEM)
• Train and scale AI models on HPC infrastructure
• Collaborate with interdisciplinary research and engineering teams
• Use Python for simulation, modeling, and data analysis

• PhD or equivalent in Physics, Applied Mathematics, Computational Science, or related field
• A background in numerical simulation and scientific ML, or strong background in generative AI and model deployment
• Experience with large-scale simulations and HPC environments
• Publication or equivalent research impact