Member of Technical Staff - Foundation Model Architecture & AI Infrastructure

Responsibilities

• 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.

Requirements

• 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.

Benefits

Additional Information

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.

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Company Intel

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