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 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.