Design Automation Engineer

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs.

Cerebras' current customers include global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

As a valued member of our close-knit physical design team, you will be responsible for a range of tasks related to Physical Design automation. This includes working on projects involving scripted placement, routing, library cell characterization, and providing support for server infrastructure.

• 10+ years of flow development and or scripting for physical design.
• Expert at generating structured placement and routing.
• Expert at characterization automation.
• Expert in scripting languages like Tcl and Python. Ability to develop PD flows, tools and augmentation scripts.
• Experience with the complete physical design flow. Knowledge of Synopsys tool suite is a plus.
• Good understanding of full chip floorplanning and integration.
• Good knowledge of server infrastructure and experience with integrating AWS and internal compute resources.
• Demonstrated ability to work with RTL teams to optimize for physical design.
• Excellentanalytical, and problem-solving skills.
• Good interpersonal skills and ability & desire to work as a standout colleague are a must.
• Extremely self-motivated and eager to solve problems.
• BS or MS in Electrical Engineering, Computer Science or similar.