Computational Scientist

• 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