About the role
We are looking
for an experienced GPU Programming Engineer to develop, optimize, and deploy
GPU-accelerated solutions for high-performance deep learning workloads. The
role focuses on GPU programming, parallel computing, performance tuning, and
deep learning system integration across platforms such as NVIDIA CUDA, AMD
ROCm/HIP, and OpenCL.
Responsibilities:
• Develop, optimize, and maintain GPU-accelerated
components for deep learning pipelines using 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.
• Work closely with system architects, software
engineers, and domain experts to integrate GPU-accelerated solutions.
Requirements
Education:
• 2+ years of hands-on experience in GPU
programming, preferably using CUDA, HIP, OpenCL, or similar GPU APIs.
• Strong understanding of GPU architecture, memory
hierarchy, shared memory, bank conflicts, 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.
Need to have (Can be bridged):
• Knowledge and experience in SIMD programming.
• Good understanding of neural network (NN)
operators and hands-on experience with PyTorch, TensorFlow, or TensorRT.
Good to have (Not essential):
• Exposure to deep learning concepts such as
quantization and pruning.
• Experience working with High Performance
Computing (HPC) systems.
Preferred Qualifications (Optional):
Additional
experience in GPU performance engineering, deep learning system optimization,
or heterogeneous computing would be advantageous.