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MEMBER OF TECHNICAL STAFF - FOUNDATION MODEL ARCHITECTURE & AI INFRASTRUCTURE Vinci | Full-Time | Remote / Hybrid THE MISSION 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 bu