GPU Engineer

Role & responsibilities

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.
  

Preferred candidate profile

• Bachelor's or master's degree in computer science, Electrical Engineering, or a related field.
  
• 2+ 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.