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Salary
$150k – $250k per year
Location
In office (Mountain View)
Seniority
Junior · 1+ year exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Sep 27, 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 are proposing new materials and synthesis recipes faster than any lab on earth 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 Computational Materials Engineer at Discovered Materials, you will:

  • Model the effect of surfaces, interfaces, grain boundaries, defects on material properties
  • Build finite element simulations to predict the impact of material properties on overall device performance.
  • Develop computational methods that that guide and validate synthesis recipes.

You will thrive here if:

  • You've built models of complex systems and validated them against real data
  • You’re comfortable with simulations across length scales, atomistic to continuum finite element models.
  • You enjoy working with fast-paced, ambitious teams and on problems with no written playbook

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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