DeepSim, Inc.
An AI physics simulator for AI chip design
What DeepSim, Inc. does
DeepSim is building an AI physics simulator. We are currently developing the only thermal simulator to meet AI chip design needs and are validating our tool with Intel. We are a team of three electrical engineering PhDs from Stanford with backgrounds in semiconductor fabrication and design.
2 open roles
What the role involves
We are looking for a Fluid Simulation Engineer with deep expertise in numerical simulation of fluid flow. You’ll play a key role in advancing DeepSim’s core simulation engine by designing solvers, modeling complex flow regimes, and enabling high-performance inference for large-scale engineering use cases. Responsibilities Develop, implement, and optimize CFD/FEM-based fluid solvers for convection and heat transfer PDEs with multiphysics interactions. Investigate and enhance solver stability, accuracy, and performance across a variety of regimes and geometries. Build core simulation components and modeling pipelines that support AI-driven simulation frameworks. Collaborate with AI and systems engineers to create hybrid numerical/ML simulation architectures. Evaluate new numerical techniques, discretization strategies, and high-performance computing optimizations. Qualifications Strong background in fluid simulations using CFD or FEM methods. Deep understanding of numerical solver mechanics and experience building CFD/FEM tools from scratch or modifying existing open-source frameworks. Proficiency in Python and C/C++, with experience writing high-performance and maintainable scientific code. Familiarity with HPC environments, GPU acceleration, or distributed computing is a plus. Master’s or PhD in engineering, applied physics, or a related discipline.
What the role involves
DeepSim is seeking an AI Physics Engineer to help build the next generation of fast, scalable physics simulation technology. This role is ideal for someone who has expertise at the intersection of modern AI models and physics-based modeling and who is excited to transform how engineers simulate complex physical systems. Responsibilities Design and implement AI-accelerated physics models with a focus on thermal, mechanical, or fluid domains. Train, evaluate, and refine machine learning models to improve simulation speed, accuracy, stability, and generalization. Develop high-quality synthetic dataset generation pipelines. Contribute to research directions by identifying opportunities for new modeling approaches, data strategies, and performance improvements. Collaborate closely with physics, AI, and platform engineers to integrate models into production-grade simulation tools. Qualifications Strong background in building and training AI or ML models, including dataset creation and preprocessing for scientific/engineering applications. Experience in physics simulations, ideally for thermal, solid mechanics, or fluid flow problems. Background in numerical methods and experience developing numerical analysis tools (especially for physics PDEs) are a big plus. Solid foundation in linear algebra. Proficiency with scientific computing tools (e.g., NumPy, PyTorch, JAX, or similar). Master’s or PhD in engineering, computer science, physics, applied math, or a related field. Comfort working in fast-paced research and production environments.
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Company facts compiled from public sources and last refreshed 9 September 2026. Details change; treat the company’s own site as the authority.