Member Of Technical Staff - Extreme-Scale Sparse Linear Algebra, Domain Decomposition & GPU Solver Architecture

Vinci4d•Palo Alto, CA

44d•Hybrid

About The Position

At Vinci, we are building the AI-enabled infrastructure that modern hardware programs use to converge on physics decisions with confidence. Our software delivers manufacturing-resolution physics simulation with verified accuracy at orders-of-magnitude faster runtimes than traditional tools, bypassing meshing and approximation overhead entirely. We are deployed or in active validation with a broad range of Tier-1 ecosystem players — across semiconductor IDMs, foundries, advanced packaging, fabless companies, automotive, EMS, and energy hardware development. This means real solver constraints, not benchmarks. Simulation decisions here drive actual hardware outcomes, with diverse operator structures and conditioning regimes. Now we are building the core solver substrate that must scale beyond billions of DOFs — to trillions, preserve determinism, and generalize across radically different operator landscapes and distributed environments. The Challenge This role is about the core numerical substrate, not application wrappers: Conditioning and convergence at extreme scale Domain decomposition and Schwarz theory at production scale Robust, multilevel and multigrid, preconditioning Communication-avoiding Krylov and hierarchical solvers Deterministic parallel reductions across GPU clusters AI-accelerated solver components grounded in numerical rigor Your work will shape the solver architecture that supports not just a single physics, but a rich operator ecosystem including indefinites, saddle-point systems, strong coefficient jumps, anisotropy, and tightly coupled multiphysics blocks encountered in real hardware workflows. What You Will Build You will own the design and delivery of production-grade solver infrastructure, including: Domain Decomposition & Schwarz Methods Additive and multiplicative Schwarz frameworks Overlapping and non-overlapping strategies Scalable coarse space construction Hybrid coarse/fine hierarchies for production meshes Preconditioning at Extreme Scale Algebraic and geometric multigrid Block/physics-aware preconditioners ILU variants, sparse approximate inverses Communication-efficient preconditioner designs Krylov & Solver Architecture CG, GMRES/FGMRES, BiCGStab Pipelined/communication-reducing methods Mixed-precision strategies with robustness guarantees Deterministic reduction ordering over distributed execution AI-Augmented Solver Enhancements Learned augmentations for coarse space discovery Adaptive preconditioner selection Spectral approximations and operator compression AI here supports numerical structure, not replaces it. What We’re Looking For You bring deep expertise in: Domain decomposition and Schwarz methods Multilevel solvers and scalable preconditioning Large sparse systems at extreme scale Parallel numerical stability and conditioning GPU-accelerated sparse linear algebra (CUDA + HIP) Multi-GPU and distributed execution paradigms You think about: Spectral equivalence and coarse space quality Strong/weak scaling tradeoffs Communication vs computation balance You’ve shipped real solver infrastructure — not just prototypes. Systems & Engineering Expectations CUDA first, HIP appreciated Kernel-level performance engineering Multi-GPU scaling experience Strong CI, regression, and correctness validation disciplines You understand how algorithms map to hardware and survive production pressure. Shipping Focus This is an execution-oriented principal engineering role in a startup with real production deployment. You will: Architect foundational solver systems Implement and ship into Tier-1 environments Build continuous validation and regression frameworks Improve throughput and determinism under real constraints We are ambitious — but we ship solutions that matter. Why Vinci Already proven at scale with real validation across Tier-1 ecosystem participants. Physics-first software built on verified methods, not heuristics. A small, technically serious team with deep domain expertise. High ownership, equity participation Production impact — not academic benchmarks If you think: Trillion-DOF problems are architectural — not just hardware — Deterministic, robust solver substrates are the heart of future physics infrastructure AI should augment numerical authority, not override it This role was designed for you. Bottom Line We are building the solver core that enables deterministic physics infrastructure — validated inside real hardware workflows and ready to scale beyond today’s limits.