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Salary
$150k – $250k per year
Location
In office (Mountain View)
Employment
Full-Time

Confirmed on the employer's own hiring board on Sep 26, 2026. First seen by Alion on Sep 25, 2026.

Overview
Company
Impact
Profile match
We introduce Discovered Materials: AI agents that discover new materials for semiconductor chips. Backed by a $9M seed round led by Lightspeed, we're also open-sourcing Material Discovery Bench.

The Problem

Our models today generate new materials and synthesis recipes faster than any lab can validate them. Many of these materials and recipes are dead ends, but a few are genuinely interesting and could lead to a novel breakthrough.

The role

As a Founding Process Engineer at Discovered Materials, you will:

  • Synthesize materials across multiple classes using PVD techniques
  • Build feedback loops using experimental data to filter out good, unique recipes that are worth trying from millions of bad ones
  • Help stand up the lab: We’ll start at UC Berkeley’s or Stanford’s nanolabs while our systems come online

You will thrive here if

  • You’ve worked with co-sputter or other combinatorial techniques
  • You’ve tried new synthesis approaches, and have an ability to set up experiments that will validate or disprove new techniques
  • You enjoy working with fast-paced, ambitious teams and on new problems

Who we are

We’re Advaith and Akash. Akash has a PhD in Material Science from Stanford University, and has spent the last 11 years researching new materials for semiconductor chips. His work on new nanoscale interconnects was Stanford Engineering’s most popular story of 2025. Advaith studied AI at Carnegie Mellon and was a research engineer building video models and agents at Persona AI (acquired) and Luma Labs. We met over 10 years ago at IIT-Madras. We’re backed by Lightspeed, Y Combinator and angels from Anthropic and OpenAI

Our mission

At Discovered Materials, we build AI agents to discover new materials for the semiconductor industry.

Chips today are at least 10,000x less power efficient than the human brain. We aim to integrate new materials into chips that close this gap.

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