Member of Technical Staff

Foundation Model Architecture & Ai Infrastructure

Vinci | Full-Time | Remote / Hybrid

The Mission

• 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
• 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

The Operator Frontier

• Maxwell's equations
• Elasticity
• Plasticity
• Navier–Stokes
• Nonlinear constitutive systems
• Coupled multiphysics interactions

What You Will Own

• 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
• 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
• Scale to trillion-voxel domains
• Use sparse and hierarchical computation effectively
• Balance memory, compute, and communication
• Maintain production-grade stability and determinism
• Ship expanded operator capabilities into production
• Increase simulations per day by 100×
• Support global, multi-entity deployment
• Maintain robustness under diverse industrial workloads

What We're Looking for

• 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

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

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