Job Opportunities API

MEMBER OF TECHNICAL STAFF - EXTREME-SCALE SPARSE LINEAR ALGEBRA, DOMAIN DECOMPOSITION & GPU SOLVER ARCHITECTURE Vinci | Full-Time | Remote / Hybrid THE MISSION 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 framewo