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.