Member of Technical Staff - Extreme-Scale Sparse Linear Algebra, Domain Decomposition & GPU Solver ArchitectureVinci | Full-Time | Remote / Hybrid
The ChallengeThis 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 BuildYou 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 ForYou 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 FocusThis 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.
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 LineWe are building the solver core that enables deterministic physics infrastructure - validated inside real hardware workflows and ready to scale beyond today's limits.
