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 the deep learning workload.
Cerebras is building a team of exceptional people to work together on big problems. Join us!
Cerebras Toronto
Cerebras is based in Sunnyvale, California, with its second engineering hub – the AI Centre of Excellence – located in Downtown Toronto. Toronto houses our Machine Learning and Software organization and has some of the most talented ML, optimization, and high-performance computing talent in the world. We have already built out an experienced team of over 50 engineers and computer scientists that are driving forward the next generation of our machine learning stack.
About The Role
As a member of our Compiler team, you will work with leaders from industry and academia to develop entirely new solutions for the toughest problems in AI compute. As deep neural network architectures evolve, they are becoming enormously parallel, and distributed. Compilers are needed to optimize the mappings of computation graphs to compute nodes. In this position, you will build the tools that generate distributed memory code from evolving intermediate representations.
Responsibilities
- Design and devise graph semantics, intermediate representations, and abstraction layers between high-level definitions (like MLIR) and low-level (LLVM IR) distributed code
- Use state-of-the-art parallelization and partitioning techniques to automate generation of distributed kernels
- Low-level optimization on a SIMD/tensor-aware architecture of compute nodes
- Identify, design and implement novel program analysis and optimization techniques
- Design and implement custom system tools (such as linkers) for architectures with massive number of compute nodes
Requirements
- Enrolled in the University of Toronto's PEY program with a degree in Computer Science, Computer Engineering, or other related disciplines
- High proficiency in programming using Python or C++
- Solid understanding of fundamental concepts related to system design, such as data structures, algorithms, and operating systems.
- Related experience or fundamental knowledge of compilers and distributed systems
- Familiarity with high-level parallel program analysis and optimization
Preferred
- LLVM compiler internals
- Polyhedral models
- Familiarity with HPC kernels and their optimization
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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