Kernel Engineer - New Grad

Summary

Cerebras Systems builds the world's largest AI chip, transforming the user experience of AI applications. As a Kernel Engineer, you will develop high-performance software for cutting-edge AI and high-performance computing workloads, collaborating with various engineering teams to optimize and validate machine learning and linear algebra operations.

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 techniques and the Cerebras Software Language, a custom C-like language
• Apply parallel programming algorithms to map computational workloads efficiently onto the Cerebras architecture
• Use mathematical analysis, performance data, and profiling tools to evaluate kernel behavior and inform design decisions
• Identify and investigate correctness, performance, and hardware utilization issues
• Develop unit tests and system-level validation methodologies to verify the functionality and performance of kernel libraries
• Collaborate with kernel, compiler, performance, and hardware engineers to improve software and system performance
• Study emerging machine learning workloads and contribute to the evolution of the kernel library
• Participate in code reviews, technical discussions, and software development processes
• Build an understanding of the Cerebras architecture, instruction set, memory system, and communication model

Skills

• Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field
• Strong programming fundamentals in C++ and familiarity with Python
• Understanding of foundational computer architecture concepts such as processors, memory hierarchies, instruction execution, or data movement
• Knowledge of data structures, algorithms, and software development fundamentals
• Experience debugging software through coursework, internships, research, co-op placements, or technical projects
• Strong analytical and problem-solving skills
• Interest in low-level software, parallel computing, performance optimization, or hardware/software co-design
• Ability to learn unfamiliar systems and collaborate effectively within a technical team
• Research, internships, or projects involving kernel development, compilers, computer architecture, HPC, or systems programming
• Familiarity with parallel algorithms, multithreaded programming, or distributed memory systems
• Exposure to programming accelerators such as GPUs, FPGAs, or other specialized processors
• Experience with low-level programming, assembly language, CUDA, OpenCL, or a domain-specific language
• Familiarity with machine learning concepts, neural networks, or frameworks such as PyTorch or TensorFlow
• Exposure to numerical computing, linear algebra, or HPC kernels
• Experience using profiling, benchmarking, or performance analysis tools
• Familiarity with library or API development practices

Qualifications

Must Haves

• Bachelor's, Master's, or PhD in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field
• Strong programming fundamentals in C++ and familiarity with Python
• Understanding of foundational computer architecture concepts such as processors, memory hierarchies, instruction execution, or data movement
• Knowledge of data structures, algorithms, and software development fundamentals
• Experience debugging software through coursework, internships, research, co-op placements, or technical projects
• Strong analytical and problem-solving skills
• Interest in low-level software, parallel computing, performance optimization, or hardware/software co-design
• Ability to learn unfamiliar systems and collaborate effectively within a technical team

Nice to Haves

• Research, internships, or projects involving kernel development, compilers, computer architecture, HPC, or systems programming
• Familiarity with parallel algorithms, multithreaded programming, or distributed memory systems
• Exposure to programming accelerators such as GPUs, FPGAs, or other specialized processors
• Experience with low-level programming, assembly language, CUDA, OpenCL, or a domain-specific language
• Familiarity with machine learning concepts, neural networks, or frameworks such as PyTorch or TensorFlow
• Exposure to numerical computing, linear algebra, or HPC kernels
• Experience using profiling, benchmarking, or performance analysis tools
• Familiarity with library or API development practices