Kernel Engineer - New Grad

About the role

Cerebras Systems builds the world's largest AI chip, a custom massively parallel processor called the Wafer-Scale Engine. As a Kernel Engineer, you will develop high-performance software at the intersection of hardware and software for AI and high-performance computing workloads. You will help implement, optimize and validate machine learning and linear algebra operations, working alongside experienced kernel, compiler, performance and hardware engineers. The role suits a new graduate interested in computer architecture, parallel programming, low-level software and machine learning systems.

Key responsibilities

• Help design and implement machine learning and linear algebra kernels for the Cerebras Wafer-Scale Engine.
• Develop and debug high-performance kernel routines using low-level programming and the Cerebras Software Language, a custom C-like language.
• Apply parallel programming algorithms to map workloads efficiently onto the Cerebras architecture.
• Use mathematical analysis, performance data and profiling tools to evaluate kernel behavior.
• Investigate correctness, performance and hardware utilization issues.
• Develop unit tests and system-level validation methods for kernel libraries.
• Collaborate with kernel, compiler, performance and hardware engineers to improve software and system performance.
• Study emerging machine learning workloads and take part in code reviews and technical discussions.

Required profile

• Bachelor's, Master's or PhD in Computer Science, Computer Engineering, Electrical Engineering, Mathematics or a related field.
• Experience debugging software through coursework, internships, research, co-op placements or technical projects.
• Strong analytical and problem-solving skills, interest in low-level software and parallel computing, and ability to learn unfamiliar systems.

Required skills

• Programming fundamentals in C++ and familiarity with Python.
• Understanding of computer architecture concepts such as processors, memory hierarchies, instruction execution and data movement.
• Knowledge of data structures, algorithms and software development fundamentals.
• Preferred: kernel development, compilers, HPC or systems programming projects; parallel algorithms, multithreading or distributed memory; GPUs, FPGAs or accelerators; CUDA, OpenCL or assembly; PyTorch or TensorFlow; profiling and benchmarking tools.