# Computational Scientist at Voltai

Computational Scientist at Voltai in Palo Alto Office. Full-time. Posted February 19, 2026.

- Company: Voltai
- Location: Palo Alto Office
- Job type: Full-time
- Posted: 2026-02-19
- Apply: https://jobs.ashbyhq.com/voltai.careers/cbf5159b-37f9-44e6-9300-d1dee6335246/application
- Source: https://jobmagic.ai/jobs/computational-scientist-voltai-r24eak

## Description

**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