This role involves designing and enhancing compute libraries for neural network operations, focusing on functional coverage and performance optimization on Rebellions' proprietary ISA. Key qualifications include a Master's degree in EE/CS, deep knowledge of neural network operations, strong troubleshooting and optimization skills, and proficiency in C++ and Python.
Good To Have:
Thorough knowledge of deep learning models for various applications, including vision, language, speech, etc.
Experience in model/layer-level customization in terms of computation efficiency (e.g., sparsity, reduced precision, layer decomposition, etc.)
Experience in architecture-specific parallel programming to accelerate target operations (e.g., SSE/AVX in x86, NEON in AArch, CUDA/OpenCL in GPU, etc.)
A major in computer architecture field is preferred
Must Have:
Master's or higher degree in Electrical Engineering, Computer Science, or a related field
Thorough knowledge of neural network operations, from high-level concepts to low-level computation flow
Excellent troubleshooting, problem-solving, and in-depth optimization skills
Proficiency in C++ and Python programming languages
Add these skills to join the top 1% applicants for this job
problem-solving
cpp
talent-acquisition
game-texts
cuda
opencl
deep-learning
python
Responsibilities and Opportunities
Designing a compute library (such as blas, dnn, etc.) composed of various neural network operations, which are being accelerated on the rebellions' proprietary instruction set architecture (ISA)
From a functionality perspective, enhancing the functional coverage of each operation by considering operation-specific constraints (e.g., tensor shape variation, precision loss handling, etc.)
From a performance perspective, enhancing the utilization of the computational units in heterogeneous compute resources by considering operation-specific characteristics
Key Qualifications
Master's or higher degree in Electrical Engineering, Computer Science, or a related field
Thorough knowledge of neural network operations, not only for the high-level concepts but also for the low-level computation flow
Thorough knowledge of deep learning models for various applications, including vision, language, speech, etc.
Experience in model/layer-level customization in terms of computation efficiency (e.g., sparsity, reduced precision, layer decomposition, etc.)
Experience in architecture-specific parallel programming to accelerate target operations (e.g., SSE/AVX in x86, NEON in AArch, CUDA/OpenCL in GPU, etc.)
A major in computer architecture field is preferred
The application process may vary by job, and may change depending on the schedule and circumstances.
Application schedule and results will be individually notified via the email address provided during application.
Notes
This announcement may close early if recruitment is completed.
If there are false facts in the application, acceptance may be canceled.
Recruitment may be restricted if legal qualifications required for recruitment and job performance are not met.
Being a person of national merit or a person with a disability does not cause any disadvantage in the recruitment process.
The scope of duties may be changed considering the candidate's overall career, experience, and other circumstances. If such a change is necessary, it will be communicated with the candidate at an appropriate time before the final acceptance notification.
For recruitment-related inquiries, please contact the email address below.
recruit@rebellions.ai
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