Chip Architect

Posted 18 Days Ago
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San Francisco, CA, USA
In-Office
Senior level
Artificial Intelligence • Machine Learning • Semiconductor • Automation
The Role
Lead microarchitecture and subsystem design for edge compute chips, owning PPA, timing, area, and power tradeoffs; establish verification and signoff practices; encode design judgment into tooling; use AI and frontier models to accelerate development; collaborate with founders and researchers to drive chips to tapeout.
Summary Generated by Built In

About Phinity

Phinity is building prompt-to-silicon: the agent infrastructure that enables AI agents to design, verify, optimize, and ultimately tape out chips. By 2028, any company will be able to specify a set of requirements for a chip and Phinity will deliver them a GDS file in weeks, not years.

Today, chip development requires large teams, fragmented tools, and multi-year development cycles. We are building a closed-loop system in which models can reason about hardware, use engineering tools, learn from verification feedback, and perform increasingly consequential parts of the chip-development process.

We work with leading frontier AI labs and are building a small team with deep experience across AI research, silicon architecture, design, and verification. Our long-term goal is autonomous tapeout, making custom silicon accessible enough to accelerate technological progress at breakneck speed.

The Role

The Chip Architect role is for an experienced silicon engineer who wants to architect and lead a a chip, heavily using AI to speed up development. Some example responsibilities include:

  • Architect and drive the development of edge compute chips, owning the microarchitecture and the PPA, timing, area, power, and physical-feasibility tradeoffs that define a good design

  • Make the chip- and subsystem-level architecture decisions that have to survive verification, timing, area, power, and physical constraints, and own the technical bar across the design

  • Establish rigorous verification and signoff practice across simulation, formal, and physical design

  • Articulate why a design decision is good, and help us encode that judgment into the tooling that lets a small team design hardware at unusual speed

  • Work hands-on with unreleased frontier models as part of how the chip gets built

The role will involve you working directly with the founders and collaborating frequently with frontier lab researchers.

Requirements

  1. 8+ years architecting and shipping complex chips or subsystems, with designs taken through tapeout

  2. Knowledge across the design stack: RTL and microarchitecture, PPA optimization, verification, understanding of physical design and signoff

  3. Experience using AI to develop chips

  4. Strong communication skills and willingness to try new methods in ambiguous and fast-moving environments

Why you should work with us

→ Build a real chip with unlimited AI usage

→ Access non-publicly released foundation models and learn what the frontier of intelligence is before everyone else

→ Own architecture decisions for novel chip design end-to-end

We offer competitive salary and equity. We work in-person from SF and also have benefits including:

  • Health insurance

  • Retirement plans

  • Unlimited PTO

  • Paid parental leave

  • Lunch and dinner provided in-office

 

Skills Required

  • 8+ years architecting and shipping complex chips or subsystems, with designs taken through tapeout
  • Knowledge across the design stack: RTL and microarchitecture, PPA optimization, verification, physical design and signoff
  • Experience using AI to develop chips
  • Strong communication skills and willingness to try new methods in ambiguous and fast-moving environments
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The Company
7 Employees
Year Founded: 2025

What We Do

Phinity Labs is building foundational training infrastructure for autonomous chip design. Its AI agents are intended to design, verify, and tape out custom chips end-to-end, while the company develops environments and data to teach large language models hardware workflows. The mission is to overcome the scarcity of open chip-design training data and enable more efficient, domain-specialized accelerators and broader hardware automation.

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