Cerebras is a computer systems company dedicated to accelerating deep learning. The pioneering Wafer-Scale Engine (WSE) – the largest chip ever built – is at the heart of its deep learning system, the Cerebras CS-1.

Analytics

Computer Hardware

Cerebras Systems

The Toronto office focuses on software engineering and technical leadership, driving innovation in machine learning and engineering solutions.

Toronto

Cerebras Systems' headquarters in Los Altos, CA, United States

The Sunnyvale office focuses on engineering and technical roles, specializing in data centers and advanced hardware design.

Sunnyvale

This department focuses on the design, development, and maintenance of software applications and systems.

Software Engineering

This department is responsible for engaging in technical projects and contributing to the development and maintenance of products.

Technical Staff

The Engineering and Technical Staff at Cerebras Systems design, develop, and optimize the hardware and software components of the pioneering Wafer-Scale Engine (WSE) and the Cerebras CS-1 deep learning system. This team specializes in areas such as chip design, machine learning, software development, and system engineering to accelerate deep learning performance and solve complex computational challenges.

Engineering and Technical Staff

The Engineering and Technology team at Cerebras Systems spearheads the design, development, and optimization of the company's core product, the Wafer-Scale Engine (WSE) and the Cerebras CS-1 system. This team includes experts in hardware and system architecture, software engineering, mechanical design, and machine learning integration. They work collaboratively to push the boundaries of deep learning performance by creating innovative hardware and software solutions.

Engineering and Technology

This department focuses on designing, implementing, and supporting machine learning solutions and technologies.

Machine Learning Development

This department focuses on developing and implementing machine learning models and solutions to enhance organizational capabilities.

People (38)

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Other teams

Technical Staff

Engineering and Technical Staff

Engineering and Technology

Machine Learning Development

Applied Machine Learning