Member of Technical Staff - Foundation Model Architecture & AI Infrastructure

Vinci4d•Palo Alto, CA

5d•Hybrid

About The Position

At Vinci, we are building the operator intelligence infrastructure that modern hardware programs rely on daily. We have already proven that a single foundation model works out of the box across industries on realistic production workloads. Trained on 45TB+ of structured physics data Running billion-voxel inference in production Deployed inside Tier-1 semiconductor and hardware environments Operating across multiple physical scales and operator regimes This is not a research prototype. This is production infrastructure. Now we are scaling deployment at industrial magnitude: Increase simulation throughput by two orders of magnitude Move from billion-voxel to trillion-voxel domains Expand operator coverage across nonlinear regimes Support global, multi-entity deployment across Tier-1 ecosystems Our ambition is not to become a frontier AI lab. Our ambition is to become the default operator intelligence layer that hardware companies run on. The Operator Frontier Today, our unified model already operates across a subset of partial differential equations in real industrial environments. The next phase is expanding that unified architecture across operators, including: Maxwell’s equations Elasticity Plasticity Navier–Stokes Nonlinear constitutive systems Coupled multiphysics interactions We are not building separate models per equation. We are evolving a single operator foundation model that generalizes across industries, physical scales, and conditioning regimes - and scales in deployment volume. What You Will Own This role is about AI architecture and systems engineering - not low-level GPU kernel work. You will help define and scale the core operator intelligence layer. Evolve the Foundation Architecture Design and refine transformer variants for structured spatial domains Explore sparse and locality-aware attention mechanisms Build hierarchical attention across multi-resolution fields Develop graph-transformer systems for multi-entity interactions Improve modeling depth across nonlinear operator regimes This is architectural ownership. Scale Training & Continuous Learning Expand distributed training beyond 45TB-scale datasets Improve generalization across heterogeneous operator distributions Design scalable data and curriculum strategies Maintain reproducibility and determinism across distributed systems Build feedback loops from deployed production environments The system must grow in capability without fragmenting in design. Architect Trillion-Scale Inference Billion-voxel inference runs today. You will help design systems that: Scale to trillion-voxel domains Use sparse and hierarchical computation effectively Balance memory, compute, and communication Maintain production-grade stability and determinism Throughput and reliability matter equally. Ship at Industrial Scale Our models already run inside Tier-1 hardware programs. You will: Ship expanded operator capabilities into production Increase simulations per day by 100× Support global, multi-entity deployment Maintain robustness under diverse industrial workloads Success is measured by adoption, throughput, and reliability — not leaderboard metrics. What We’re Looking For Deep experience in: Large-scale foundation model architecture Transformer variants (sparse, hierarchical, graph-based) Distributed training systems Production ML system design Scaling structured datasets Writing clean, maintainable, high-quality code You think in terms of: Architectural generalization Stability under nonlinear regimes Communication vs computation tradeoffs Deterministic distributed execution Designing systems that become durable infrastructure You’ve built AI systems that run in production — not just experiments. Engineering Expectations Strong software engineering fundamentals Clean abstractions and scalable code design Experience with modern ML stacks (e.g., PyTorch and distributed training ecosystems) Strong CI, regression testing, and validation discipline Comfort evolving core model infrastructure This role is about building infrastructure that lasts. Why Vinci Single model already deployed across industries 45TB+ structured training data Billion-voxel inference in production Tier-1 customers operating on real hardware workflows High ownership at Series A stage Opportunity to define a foundational abstraction layer early We are building something that hardware companies will depend on daily. If you want to define and scale the operator intelligence layer that industry runs on — this role was built for you.

Requirements

Large-scale foundation model architecture
Transformer variants (sparse, hierarchical, graph-based)
Distributed training systems
Production ML system design
Scaling structured datasets
Writing clean, maintainable, high-quality code
Architectural generalization
Stability under nonlinear regimes
Communication vs computation tradeoffs
Deterministic distributed execution
Designing systems that become durable infrastructure
Strong software engineering fundamentals
Clean abstractions and scalable code design
Experience with modern ML stacks (e.g., PyTorch and distributed training ecosystems)
Strong CI, regression testing, and validation discipline
Comfort evolving core model infrastructure