
A challenging role for a senior GPU programmer looking to develop cutting-edge machine learning workloads
2 days ago
As a seasoned expert in GPU programming, you will be responsible for developing, optimizing, and deploying high-performance machine learning workloads on cutting-edge GPUs.
- Create and refine GPU-accelerated components for machine learning pipelines using frameworks such as CUDA, HIP, or OpenCL.
- Analyze and enhance GPU kernel performance through profiling, benchmarking, and resource optimization.
- Optimize memory access, compute throughput, and kernel execution to improve overall system performance on the target GPUs.
- Port existing CPU-based implementations to GPU platforms while ensuring correctness and performance scalability.
- Collaborate closely with system architects, software engineers, and domain experts to integrate GPU-accelerated solutions.
- Bachelor's or master's degree in computer science, electrical engineering, or a related field.
- At least 3 years of hands-on experience in GPU programming using CUDA, HIP, OpenCL, or other GPU compute APIs.
- Strong understanding of GPU architecture, memory hierarchy, and parallel programming models.
- Proficiency in C/C++ and hands-on experience developing on Linux-based systems.
- Familiarity with profiling and tuning tools such as Nsight, rocprof, or Perfetto.
The ideal candidate will have a solid foundation in computer science, electrical engineering, or a related field, along with significant experience in GPU programming and parallel computing. If you are passionate about harnessing the power of GPUs to drive innovation and excellence, we encourage you to apply for this exciting opportunity.
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