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

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.

The Challenge

This role is about the core numerical substrate, not application wrappers: Conditioning and convergence at extreme scaleDomain decomposition and Schwarz theory at production scaleRobust, multilevel and multigrid, preconditioningCommunication-avoiding Krylov and hierarchical solversDeterministic parallel reductions across GPU clustersAI-accelerated solver components grounded in numerical rigor

Your Work

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

• 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

What We’re Looking For

• You bring deep expertise in: Domain decomposition and Schwarz methodsMultilevel solvers and scalable preconditioningLarge sparse systems at extreme scaleParallel numerical stability and conditioningGPU-accelerated sparse linear algebra (CUDA + HIP)Multi-GPU and distributed execution paradigms
• You think about: Spectral equivalence and coarse space qualityStrong/weak scaling tradeoffsCommunication vs computation balance
• You’ve shipped real solver infrastructure — not just prototypes. Systems & Engineering Expectations
• CUDA first, HIP appreciatedKernel-level performance engineeringMulti-GPU scaling experienceStrong CI, regression, and correctness validation disciplines
• You understand how algorithms map to hardware and survive production pressure.

Systems & Engineering Expectations

• 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

Compensation Range

$100K - $220K