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The Lead Design Verification Engineering Manager will ensure high-quality design and validation of hardware systems at Cerebras Systems by managing a team of engineers in developing verification strategies and methodologies. The role includes overseeing test execution, debugging, continuous improvement, and cross-functional collaboration to guarantee robust performance across all stages of development.

As a Compiler Engineer at Cerebras Systems, you will design and optimize compiler technologies for AI chips using LLVM and MLIR frameworks. You will work closely with the machine learning team to enhance compiler performance, address bottlenecks, and ensure efficient resource utilization for AI applications.

As a Full-Stack Software Engineer, you will design and build user-friendly frontend interfaces for AI cloud applications, ensuring high reliability and efficiency in handling customer traffic. You will utilize web development frameworks and collaborate with teams to enhance the user experience while actively participating in a rapidly evolving environment.

The IT/DevOps Engineer will be responsible for managing corporate and engineering AI/ML environments, providing IT support, designing hardware configurations, troubleshooting issues, and maintaining cloud solutions, while also automating day-to-day tasks.

The Embedded Software Engineer will define, design, and integrate software components for an innovative embedded stack at Cerebras. Responsibilities include developing scalable and robust code, defining requirements, writing tests, and collaborating with cross-functional teams.

The Design Automation Engineer will be responsible for tasks related to Physical Design automation, including scripted placement, routing, and library cell characterization. This role requires expertise in physical design flows and strong scripting skills, with collaboration across RTL teams to optimize physical design processes.

The Data Processing Engineer will design and implement frameworks for visualizing and analyzing complex datasets, generate insights to improve system performance, and collaborate with cross-functional teams to meet their data needs. Responsibilities include ensuring data quality and integrity while querying data using SQL and processing it with Python.