Cerebras has developed a radically new chip and system to dramatically accelerate deep learning applications. Our system runs training and inference workloads orders of magnitude faster than contemporary machines, fundamentally changing the way ML researchers work and pursue AI innovation.
We are innovating at every level of the stack – from chip, to microcode, to power delivery and cooling, to new algorithms and network architectures at the cutting edge of ML research. Our fully-integrated system delivers unprecedented performance because it is built from the ground up for deep learning workloads.
The Role
In this exciting role, you will be responsible for silicon testing, repair and burn in of Cerebras wafer scale engines. We are looking for someone with a passion for productization of silicon and system tests.
Responsibilities
- Work with the silicon and operations team to test, bring-up and run burn-in on wafers scale systems
- Utilize silicon redundancy to repair wafers based on defect signatures
- Run repair algorithms and use strategies to optimize wafer performance and quality
- Debug issues found in production, to enable volume ramp of system production
- Root cause issues and recommend solutions
Skills & Qualifications
- BS or MS in EE, ECE, CS or equivalent work experience
- 5+ years of industry experience
- Solid experience in hardware bring-up and the debug of complex systems including ASICs, SoCs or microprocessors
- Excellent debugging, analytical, and problem-solving skills
- Understanding of computer architecture
- Working knowledge of scripting in languages such as Python and/or Perl
- Good understanding of DFT, ATPG, and design for debug techniques and their application in testing of silicon
- Good interpersonal skills, ability & desire to work as a standout colleague
- Proven track record of working cross-functionally and driving issues to closure
Preferred
- Knowledge of silicon failure analysis
- Background with lab equipment such as test rigs, logic analyzers and oscilloscopes
Location
North America based. Sunnyvale or Toronto preferred.
Why Join Cerebras
People who are serious about software make their own hardware. At Cerebras we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
- Build a breakthrough AI platform beyond the constraints of the GPU
- Publish and open source their cutting-edge AI research
- Work on one of the fastest AI supercomputers in the world
- Enjoy job stability with startup vitality
- Our simple, non-corporate work culture that respects individual beliefs
Read our blog: Five Reasons to Join Cerebras in 2024.
Apply today and become part of the forefront of groundbreaking advancements in AI.
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.
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Top Skills
What We Do
Cerebras Systems is a team of pioneering computer architects, computer scientists, deep learning researchers, functional business experts and engineers of all types. We have come together to build a new class of computer to accelerate artificial intelligence work by three orders of magnitude beyond the current state of the art.
The CS-2 is the fastest AI computer in existence. It contains a collection of industry firsts, including the Cerebras Wafer Scale Engine (WSE-2). The WSE-2 is the largest chip ever built. It contains 2.6 trillion transistors and covers more than 46,225 square millimeters of silicon. The largest graphics processor on the market has 54 billion transistors and covers 815 square millimeters. In artificial intelligence work, large chips process information more quickly producing answers in less time. As a result, neural networks that in the past took months to train, can now train in minutes on the Cerebras CS-2 powered by the WSE-2.
Join us: https://cerebras.net/careers/
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