Topological

Topological

Physics-based foundation models for CAD optimization.

San Francisco
2 employees
Reinforcement Learning
Seed

About Topological

Topological is developing physics-based foundation models for CAD optimization. We help hardware teams iterate at the same speed that software teams do. Our technology is accelerating the engineering workflow with AI and scales design and optimization to identify the ideal designs for complex problems given their physical constraints with enhanced speed and performance.

Our first model, UToP-v1, is a SOTA topology optimization model that understands physics, geometry, and manufacturability. It can generate the most efficient design given a problem’s physical requirements. It has <5% compliance error and is 1930x faster than current methods. We're reimagining mechanical engineering and computational design with precision spatial AI.

Company details

Team Size2
LocationSan Francisco
IndustryReinforcement Learning

Funding

Last stage

Seed

Investors

Y Combinator

Founders

ZK

Zeyneb Kaya

Building Topological (YC S25). Prev CS/Physics/Math @ Stanford; research in RL, pretraining, efficient algorithms, synthetic data, physics.

LinkedIn
Nicole Ma

Nicole Ma

CS/MechE at Stanford; previously worked on physics simulations at NASA and spatial reasoning at Topological.

LinkedIn

What happens next.

No applications, no recruiter spam. Just the intro.

01

Confirm the fit

A few questions to make sure this role is the right shape for you. Two minutes.

02

I pitch you to the company

I write the intro, send it to the founder, and handle the back-and-forth.

03

A meeting lands on your calendar

If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.