Member of Technical Staff - Foundation Model Architecture & AI Infrastructure

Other

• Vinci | Full-Time | Remote / Hybrid
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• Strong software engineering fundamentals