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1 week ago
We are seeking a highly skilled and experienced GPU programming engineer to join our team. This role involves developing, optimizing, and deploying GPU-accelerated solutions for high-performance machine learning workloads.
The ideal candidate has strong expertise in GPU programming across multiple platforms, including NVIDIA CUDA, AMD ROCm/HIP, or OpenCL, and is comfortable working at the intersection of parallel computing, performance tuning, and ML system integration.
**Key Responsibilities**- Develop, optimize, and maintain GPU-accelerated components for machine learning pipelines using frameworks such as CUDA, HIP, or OpenCL.
- Analyze and improve GPU kernel performance through profiling, benchmarking, and resource optimization.
- Optimize memory access, compute throughput, and kernel execution to improve overall system performance on 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.
- 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.
- Familiarity with cuDNN, TensorRT, OpenCL, or other GPU computing libraries.
- Exposure to AI/ML workloads, computer vision, or video/image processing.
- Experience with multi-GPU programming or heterogeneous computing environments.
- Knowledge of OpenACC, SYCL, or other directive-based programming models is a plus.
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Technical Computer Specialist
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Advanced Cloud Architectural Specialist
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