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Kernel Developer

Eerpoland
CompanyEerpoland
CategoryEngineering
Location
Remote
EmploymentNot stated
LevelNot stated
SalaryNot stated by the employer
Posted22 May 2026
Last verified30 Jul 2026
SourceEmployer career page (teamtailor)
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Description
Kernel Developer About the Role We are looking for a C/C++ engineer to design and optimize high-performance compute kernels for AI accelerators . This is not Linux kernel development , and it does not involve drivers, operating systems, or kernel modules. In this context, “kernels” refer to user-space compute kernels for tensor operations used in deep learning workloads . You will work at the lowest level of AI performance engineering — where software meets specialized hardware. Responsibilities Design and implement high-performance compute (operator) kernels in C/C++ Develop core tensor operations Optimize performance for AI accelerators (latency, throughput, and efficiency) Apply low-level optimization techniques Profile, benchmark, and tune kernels to eliminate performance bottlenecks Contribute to internal libraries and runtime systems for AI workloads Requirements Strong proficiency in C/C++ Experience with performance-critical software development Strong understanding of low-level optimization techniques Understanding of CPU/GPU or accelerator architecture fundamentals Ability to analyze and debug complex systems Experience working with large, complex codebases Strong communication and teamwork skills Nice to Have Experience with GPU kernel programming (CUDA / ROCm / OpenCL) Experience with Triton or similar kernel programming frameworks Knowledge of instruction set architectures (ISA) Familiarity with compiler technologies (e.g., LLVM-based stacks) Experience with distributed communication frameworks (NCCL, MPI, libfabric) Understanding of deep learning models What We Offer Highly competitive salary, employment contract (Umowa o Pracę), and a comprehensive benefits package, including Medicover healthcare coverage. Work on the performance-critical compute layer for next-generation AI accelerators Direct impact on deep learning model efficiency and latency Collaboration with experts in hardware, compilers, and systems Challenging low-level performance engineering problems at the hardware–software boundary
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