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.