Design Verification Intern, Lightmatter
$35 - $45 USD per hour
Light-powered AI computing
Job no longer available
Light-powered AI computing
201-500 employees
Job no longer available
$35 - $45 USD per hour
201-500 employees
Company mission
To build an engine that will power discoveries, drive progress, and redefine what computers and human beings are capable of.
Role
Who you are
- If you're passionate about tackling complex challenges, making an impact, and being an expert in your craft, join our team of brilliant scientists, engineers, and accomplished industry leaders
- Currently enrolled in a graduate engineering program, preferably in an ECE, EE or Computer Engineering degree
- Proficiency in Hardware Description Languages (HDL) such as SystemVerilog
- Knowledge of verification methodologies such as UVM
- Proficiency in using simulation tools like Xcelium, ModelSim, Questa, or VCS
- Experience with Scripting Languages (perl or python)
- Strong problem-solving skills to identify and resolve issues in the design
- Exposure to AMS verification
- Exposure to formal verification
- Fluency in English, both written and verbal
- Ability to work with diverse teams of engineers and scientists
What the job involves
- We are hiring an intern for our Design Verification team at Lightmatter! Lightmatter has created a solution in photonic computing: using photons instead of electrons to take advantage of their higher bandwidth. We build chips for artificial intelligence computing
- Our architecture leverages the unique properties of light to enable fast and efficient inference and training engines
- Collaborate closely with digital, photonics, and analog designers to gain a thorough understanding of their designs and formulate comprehensive test plans for verifying their work
- Create UVM test benches for subsystem-level verification and provide support for full-chip verification. Debug test benches, identify and address issues, achieve high coverage, and ultimately sign off on Design Verification (DV)
- Develop Real Number Models (RNM) for photonics and analog Intellectual Properties (IPs) and conduct AMS verification to ensure accurate model representation. Integrate the RNM models seamlessly into the UVM test bench
Our take
Neural networks and CPUs are vital to AI solutions, but manufacturers have largely failed to maintain innovation in recent years. Lightmatter solves this problem by offering a next-generation computing platform powered by photonics (light waves).
Lightmatter offers a forward-facing platform for AI-building that acts as an alternative to traditional solutions. It works by replacing wired CPUs with a combination of photonics and new algorithms. Lightmatter's solution is not only faster, but it is considerably more sustainable, and it has the potential to drive the next generation of AI development.
As of early 2026, Lightmatter had raised $850 million across six funding rounds and grown to a staggering valuation of $4.4 billion. This showcases the growing demand for faster, more effective AI-building solutions. With products already on the market and a roadmap to tackle even more components in the years ahead, the future for Lightmatter certainly looks bright.
Steph
Company Specialist at Welcome to the Jungle
Insights
Company
Funding (last 2 of 6 rounds)
Oct 2024
$400m
SERIES D
Dec 2023
$155m
SERIES C
Total funding: $822m
Company benefits
- Commuter benefit
- Happy hour -Spend time together and build friendships
- Generous vacation policy and holidays
- 401(K)
- Opportunity to learn from your peers and teach them what you know across electronics, photonics, machine learning, and more
Company HQ
Old Mountain View, Mountain View, CA
Leadership
Nicholas Harris
(CEO & Co-Founder)Previously worked as an R&D Product Engineer at Micron Technology, and as a Postdoctoral Fellow at MIT.
Thomas Graham
(Head of ML & Co-Founder)Was an M&A Analyst at Morgan Stanley, and worked as an Agency Business Development Manager at Google.
Darius Bunandar
(Chief Scientist & Co-Founder)Worked as a Research Collaborator at Caltech, and was a PhD Candidate at MIT.
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