Compiler Verification Engineer

Cerebras is developing 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.

Cerebras is building a team of exceptional people to work together on big problems. Join us!

The Role

As a member of the Compiler team, you will work with leaders from industry and academia to develop entirely new solutions for the toughest problems in AI compute. We are optimizing the mapping of neural network computation to the enormously parallel and distributed nature of the Cerebras wafer-scale engine, ensuring the best performance on the AI workloads of the present and future. In this role, you will develop the tools and processes for verifying and maintaining compiler correctness and performance.

You will:

• Work closely with developers to validate compiler components by crafting unit, functional, and performance tests.
• Ensure good coverage of compiler components through quantitative coverage metrics, random testing, and robust test plans.
• Diagnose functional and performance issues by building up an understanding of the target workloads and underlying hardware architecture.
• Implement testing dashboards and integrate test runs with CI.
• Foster a culture of continuous process improvement.

Skills & Qualifications

Required:

• BS or MS degree in Computer Science, Computer Engineering, Mathematics, or related fields
• 3+ years of software development or QA experience
• Experience programming and/or testing in C++/C, assembly, and scripting languages
• Understand a variety of testing approaches (random, fuzz, unit, regression)
• Familiarity with compiler and computer architecture
• Comfort with a fast-paced environment and rapidly evolving codebase

Preferred:

• Familiarity with machine learning concepts.
• Knowledge of deep learning frameworks such as TensorFlow and PyTorch and optimizing compilers like XLA and MLIR
• Familiarity with LLVM and other compiler backends
• Formal methods experience