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.