GPU Accelerated Solutions Developer

2 days ago

Lucknow, Uttar Pradesh, India beBeeGPU Full time ₹ 20,00,000 - ₹ 25,00,000
GPU Accelerated Solutions Developer

We are seeking an experienced professional to develop, optimize, and deploy GPU-accelerated solutions for high-performance machine learning workloads.

  • 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 the target GPUs.
  • Port existing CPU-based implementations to GPU platforms while ensuring correctness and performance scalability.
  • Work closely with cross-functional teams to integrate GPU-accelerated solutions.
Required Skills and Qualifications
  • 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.
PREFERRED QUALIFICATIONS
  • 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.
About the Role

This role offers an opportunity to contribute to the development of cutting-edge GPU-accelerated solutions that drive innovation in machine learning and beyond.

A successful candidate will possess strong technical skills, excellent problem-solving abilities, and effective communication skills to collaborate with cross-functional teams.

The ideal candidate will be a self-motivated individual who thrives in a fast-paced environment and is passionate about staying up-to-date with industry trends and advancements in GPU computing.

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