Computational Scientist

About VoltaiVoltai is developing world models, and agents to learn, evaluate, plan, experiment, and interact with the physical world. We are starting out with understanding and building hardware; electronics systems and semiconductors where AI can design and create beyond human cognitive limits.About The TeamBacked by Silicon Valley’s top investors, Stanford University, and CEOs/Presidents of Google, AMD, Broadcom, Marvell, etc. We are a team of previous Stanford professors, SAIL researchers, Olympiad medalists (IPhO, IOI, etc.), CTOs of Synopsys & GlobalFoundries, Head of Sales & CRO of Cadence, former US Secretary of Defense, National Security Advisor, and Senior Foreign-Policy Advisor to four US presidents.What You'll Work OnDevelop and scale MPI+CUDA PDE solvers for electrostatics, charge transport, and electromagnetic field problems on complex 3D IC geometries across multi-node GPU clustersTune and extend AMG preconditioners, Krylov solvers, and mesh pipelines for performance and correctness at scaleBuild and train neural operators (FNO, DeepONet, GNO, and variants) as high-fidelity surrogates for PDE-based field solversDesign simulation pipelines that generate training data for neural operator models — including sampling strategies, mesh handling, and physical consistency checksValidate everything: analytical solutions, published benchmarks, and cross-validation between field solvers and learned surrogatesRequiredPhD in computational physics, applied mathematics, computational engineering, or a closely related fieldDeep expertise in numerical PDE methods: FEM, FVM, or BEM — weak formulations, quadrature, convergence, error analysisStrong C++ and CUDA — writing and optimizing kernels, memory hierarchy, multi-GPU programmingMulti-node HPC: MPI, domain decomposition, collective communication, strong/weak scalingSparse linear algebra at depth: Krylov methods, algebraic multigrid, preconditioning strategiesHands-on experience with neural operators (FNO, DeepONet, or equivalent) — training, architecture design, and evaluation on PDE datasetsSolid understanding of AI for Science methodology: how to design datasets from simulations, handle out-of-distribution generalization, and ensure physical consistency of learned modelsStrongly PreferredExperience with HYPRE, PETSc, and TrilinosFamiliarity with multi-node GPU clusters: NCCL, CUDA-aware MPI, NVLink topologiesPublished work in neural operators, physics-informed ML, or scientific HPCIC design domain knowledge: device physics, semiconductor materials, layout data formats