Member of Technical Staff - Foundation Model Architecture & AI Infrastructure

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.