AI scientists to discover materials for the semiconductor industry. Backed by Y Combinator.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- Synthesize materials across multiple classes in our organic lab in Mountain View
- Help us come up with new scientific theories by using experiments to validate hypotheses
- You’ve worked with semiconductor organic materials like resists, dielectrics, precursors, inhibitors etc
- You enjoy working with fast-paced, ambitious teams and on new problems
More about this role
- Synthesize materials across multiple classes in our organic lab in Mountain View
- Help us come up with new scientific theories by using experiments to validate hypotheses
- You’ve worked with semiconductor organic materials like resists, dielectrics, precursors, inhibitors etc.
- You enjoy working with fast-paced, ambitious teams and on new problems
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
At Discovered Materials, we use AI to develop new scientific theories and 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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