MulticoreWare - GPU Engineer - CUDA/OpenGL

<p>Job Summary :<br/><br/>We are seeking an experienced GPU Programming Engineer to join our team.

In this role, you will focus on developing, optimizing, and deploying GPU-accelerated solutions for high-performance machine learning workloads.

The ideal candidate has strong expertise in GPU programming across one or more platforms (e.g., NVIDIA CUDA, AMD ROCm/HIP, or OpenCL) and is comfortable working at the intersection of parallel computing, performance tuning, and ML system integration.<br/><br/>Key Responsibilities :<br/><br/>- Develop, optimize, and maintain GPU-accelerated components for machine learning pipelines using frameworks such as CUDA, HIP, or OpenCL.<br/><br/>- Analyze and improve GPU kernel performance through profiling, benchmarking, and resource optimization.<br/><br/>- Optimize memory access, compute throughput, and kernel execution to improve overall system performance on the target GPUs.<br/><br/>- Port existing CPU-based implementations to GPU platforms while ensuring correctness and performance scalability.<br/><br/>- Work closely with system architects, software engineers, and domain experts to integrate GPU-accelerated solutions.<br/><br/>Required Qualifications :<br/><br/>- Bachelor's or master's degree in computer science, Electrical Engineering, or a related field.<br/><br/>- 2+ years of hands-on experience in GPU programming using CUDA, HIP, OpenCL, or other GPU compute APIs.<br/><br/>- Strong understanding of GPU architecture, memory hierarchy, and parallel programming models.<br/><br/>- Proficiency in C/C++ and hands-on experience developing on Linux-based systems.<br/><br/>- Familiarity with profiling and tuning tools such as Nsight, rocprof, or Perfetto.<br/><br/>Preferred Qualifications :<br/><br/>- Familiarity with cuDNN, TensorRT, OpenCL, or other GPU computing libraries.</p> (ref:hirist.tech)