Apply to Cerebras SystemsAbout The Role
As a Kernel Engineer on our team, you will work with leaders from industry and academia at the intersection of hardware and software to develop state-of-the-art solutions for emerging problems in AI and HPC.
Our team of developers is responsible for the design, implementation, validation, and performance tuning of deep learning operations on highly parallel custom processors. We are developing a library of parallel and distributed algorithms to maximize hardware utilization and accelerate the training of deep neural networks to unprecedented speeds.
Responsibilities
- Develop design specifications for new machine learning and linear algebra kernels and mapping to the Cerebras WSE System using various parallel programming algorithms
- Develop and debug kernel library of highly optimized low level assembly instruction and C-like domain specific language routines to implement algorithms targeting the Cerebras hardware system
- Using mathematical models and analysis to measure the software performance and inform design decisions
- Develop and integrate unit and system testing methodologies to verify correct functionality and performance of kernel libraries
- Study emerging trends in Machine Learning applications and help evolve Kernel library architecture to address computational challenges of the start-of-the-art Neural Networks
- Interact with chip and system architects to optimize instruction sets, microarchitecture, and IO of next generation systems
Requirements
- Enrolled within University of Toronto's PEY program with a degree in Computer Science, Computer Engineering, or any other related discipline
- Understanding of hardware architecture concepts — must be comfortable learning the details of a new hardware architecture
- Skilled in C++ and Python programming languages
- Good knowledge of library and/or API development best practices
- Strong debugging skills and knowledge of debugging complex software stack
Preferred Skills
- Experience in kernel development and/or testing
- Familiarity with parallel algorithms and distributed memory systems
- Experience in programming accelerators such as GPUs and FPGAs
- Familiarity with Machine Learning neural networks and frameworks such as TensorFlow and PyTorch
- 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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Cerebras Systems has pioneered a groundbreaking chip and system that revolutionizes deep learning applications. Our system empowers ML researchers to achieve unprecedented speeds in training and inference workloads, propelling AI innovation to new horizons.
The Condor Galaxy 1 (CG-1), unveiled in a recent announcement, stands as a testament to Cerebras' commitment to pushing the boundaries of AI computing. With a staggering 4 ExaFLOP processing power, 54 million cores, and 64-node architecture, the CG-1 is the first of nine powerful supercomputers to be built and operated through an exclusive partnership between Cerebras and G42. This strategic collaboration aims to redefine the possibilities of AI by creating a network of interconnected supercomputers that will collectively deliver a mind-boggling 36 ExaFLOPS of AI compute power upon completion in 2024.
Cerebras is building a team of exceptional people to work together on big problems. Join us!