Kernel Engineer - New Grad

The role

You will develop high-performance software at the intersection of hardware and software for artificial intelligence and high-performance computing workloads. You will implement, optimize, and validate machine learning and linear algebra operations for the Cerebras Wafer-Scale Engine.

What you will do

• 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.
• 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.
• Identify and investigate correctness, performance, and hardware utilization issues.
• Develop unit tests and system-level validation methodologies.
• Collaborate with kernel, compiler, performance, and hardware engineers.
• 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.

Required qualifications

• 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 have

• 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.

Benefits

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Job stability with startup vitality.
• Simple, non-corporate work culture that respects individual beliefs.

About the company

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. The company works with leading model labs, global enterprises, and cutting-edge AI-native startups.