GPU Programming Engineer

Job Summary 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. 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 the target GPUs. Port existing CPU-based implementations to GPU platforms while ensuring correctness and performance scalability. Work closely with system architects, software engineers, and domain experts to integrate GPU-accelerated solutions. Required 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.