Kernel Engineer

Cerebras

. Develop design specifications for new machine learning and linear algebra kernels and map them to the Cerebras WSE System using parallel programming algorithms .

Posted 11/6/2026full-timeSunnyvale • California • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing machine learning kernels and low-level assembly routines for high-performance computing, with a strong foundation in C++ and Python programming. Familiarity with neural networks, parallel algorithms, and hardware architecture is essential for delivering cutting-edge software solutions.

Highest-signal resume keywords

C++ ProgrammingPython ProgrammingKernel DevelopmentMachine Learning FrameworksParallel Algorithms

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Applicant Tracking System Keywords

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Hard Skills

Low-Level Assembly ProgrammingDomain-Specific Language DevelopmentPerformance MeasurementDebugging Complex Software StacksUnit and System Testing

Soft Skills

Problem-SolvingCollaboration

Tools & Technologies

TensorFlowPyTorchCerebras WSE System

Industry Keywords

High-Performance ComputingNeural NetworksExport ComplianceDistributed Memory Systems

Tech Stack

Tools & technologies

AssemblyPythonPyTorchTensorflowC++

About the role

Key responsibilities & impact

Develop design specifications for new machine learning and linear algebra kernels and map them to the Cerebras WSE System using parallel programming algorithms
Develop and debug highly optimized low-level assembly instruction and C-like domain-specific language routines targeting Cerebras hardware
Develop and debug high-performance kernel routines in low-level assembly and custom C-like CSL language
Use mathematical models and analysis to measure software performance and inform design decisions
Develop and integrate unit and system testing methodologies to verify kernel-library functionality and performance
Study emerging machine learning application trends and evolve kernel-library architecture for state-of-the-art neural networks
Interact with chip and system architects to optimize instruction sets, microarchitecture, and I/O of next-generation systems
Develop high-performance software solutions for AI and HPC workloads at the intersection of hardware and software
Implement, optimize, and scale deep learning operations for Cerebras’s massively parallel processor architecture

Requirements

What you’ll need

Bachelor’s, Master’s, PhD or foreign equivalents in Computer Science, Computer Engineering, Mathematics, or related fields
Understanding of hardware architecture concepts and willingness to learn 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 stacks
Experience in kernel development and/or testing (preferred)
Familiarity with parallel algorithms and distributed memory systems (preferred)
Experience programming accelerators such as GPUs and FPGAs (preferred)
Familiarity with machine learning neural networks and frameworks such as TensorFlow and PyTorch (preferred)
Familiarity with HPC kernels and their optimization (preferred)
Must comply with applicable U.S. Export Administration Regulations and related export-compliance assessment requirements

Benefits

Comp & perks

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
Continuous learning, growth and support
Equal and diverse work environment