Member of Technical Staff - Foundation Model Architecture & AI Infrastructure
LocationPalo Alto HQ
Work modehybrid
Typefull-time
DepartmentEngineering
Company size11+ people
First seen1w ago
Last seen1d ago
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.
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Trained on 45TB+ of structured physics data
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Running billion-voxel inference in production
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Deployed inside Tier-1 semiconductor and hardware environments
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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:
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Increase simulation throughput by two orders of magnitude
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Move from billion-voxel to trillion-voxel domains
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Expand operator coverage across nonlinear regimes
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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:
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Maxwell’s equations
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Elasticity
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Plasticity
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Navier–Stokes
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Nonlinear constitutive systems
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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
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Design and refine transformer variants for structured spatial domains
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Explore sparse and locality-aware attention mechanisms
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Build hierarchical attention across multi-resolution fields
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Develop graph-transformer systems for multi-entity interactions
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Improve modeling depth across nonlinear operator regimes
This is architectural ownership.
Scale Training & Continuous Learning
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Expand distributed training beyond 45TB-scale datasets
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Improve generalization across heterogeneous operator distributions
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Design scalable data and curriculum strategies
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Maintain reproducibility and determinism across distributed systems
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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:
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Scale to trillion-voxel domains
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Use sparse and hierarchical computation effectively
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Balance memory, compute, and communication
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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:
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Ship expanded operator capabilities into production
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Increase simulations per day by 100×
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Support global, multi-entity deployment
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Maintain robustness under diverse industrial workloads
Success is measured by adoption, throughput, and reliability — not leaderboard metrics.
Designing systems that become durable infrastructure
You’ve built AI systems that run in production — not just experiments.
Engineering Expectations
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Strong software engineering fundamentals
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Clean abstractions and scalable code design
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Experience with modern ML stacks (e.g., PyTorch and distributed training ecosystems)
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Strong CI, regression testing, and validation discipline
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Comfort evolving core model infrastructure
This role is about building infrastructure that lasts.
Why Vinci
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Single model already deployed across industries
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45TB+ structured training data
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Billion-voxel inference in production
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Tier-1 customers operating on real hardware workflows
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High ownership at Series A stage
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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.