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 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.