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About Voltai Voltai 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 Team Backed 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 On - Develop and scale MPI+CUDA PDE solvers for electrostatics, charge transport, and electromagnetic field problems on complex 3D IC geometries across multi-node GPU clusters - Tune and extend AMG preconditioners, Krylov solvers, and mesh pipelines for performance and correctness at scale - Build and train neural operators (FNO, DeepONet, GNO, and variants) as high-fidelity surrogates for PDE-based field solvers - Design simulation pipelines that generate training data for neural operator models — including sampling strategies, mesh handling, and physical consistency checks - Validate everything: analytical solutions, published benchmarks, and cross-validation between field solvers and learned surrogates Required - PhD in computational physics, applied mathematics, computational engineering, or a closely related field - Deep expertise in numerical PDE methods: FEM, FVM, or BEM — weak formulations, quadrature, convergence, error analysis - Strong C++ and CUDA — writing and optimizing kernels, memory hierarchy, multi-GPU programming - Multi-node HPC: MPI, domain decomposition, collective communication, strong/weak scaling - Sparse linear algebra at depth: Krylov methods, algebraic multigrid, preconditioning strategies - Hands-on experience with neural operators (FNO, DeepONet, or equivalent) — training, architecture design, and evaluation on PDE datasets - Solid understanding of AI for Science methodology: how to design datasets from simulations, handle out-of-distribution generalization, and ensure physical consistency of learned models Strongly Preferred - Experience with HYPRE, PETSc, and Trilinos - Familiarity with multi-node GPU clusters: NCCL, CUDA-aware MPI, NVLink topologies - Published work in neural operators, physics-informed ML, or scientific HPC - IC design domain knowledge: device physics, semiconductor materials, layout data formats