Gpu model optimization engineer

3 days ago

Singapore WeRide.ai Full time

Company Introduction
We Ride is a smart mobility start-up whose mission is to transform mobility with autonomous driving. We are committed to building a better transportation experience that’s safe, efficient, affordable, and joyful. We have an elite team of entrepreneurs and technologists who share the same passion and pursue continuous excellence in their work.
For more information, please visit: , or follow the official We Chat account: We Ride (文远知行).
Job Overview:
As a GPU AI/HPC Expert Engineer, you will provide professional support in the design and implementation of cutting-edge GPU computing to address issues related to deep learning, high-performance computing, and computationally intensive workloads, focusing on optimizing capacity management and allocation within GPU computing clusters.
Locations:
Singapore
Guangzhou, Guangdong, China
Shenzhen, Guangdong, China
Responsibilities:
Performance Analysis and Optimization:Conduct in-depth analysis of GPU applications to identify and resolve performance bottlenecks, focusing on memory access patterns, thread scheduling, and execution efficiency.
Algorithm Development:Develop efficient GPU-accelerated algorithms using parallel computing frameworks such as CUDA or Direct X to enhance the processing capabilities of computationally intensive tasks.
Provide technical guidance and training to the team, sharing best practices and optimization techniques in GPU programming.
Keep abreast of GPU technology and industry trends, evaluating and integrating new technologies to improve system performance.
Requirements:
Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field.
At least 5 years of experience in GPU programming and performance optimization, familiar with CUDA or Direct X development.
Proficient in C/C++, with in-depth knowledge of computer architecture and parallel computing principles.
Proficient in using GPU performance analysis and debugging tools, with actual performance optimization achievements.
Rich experience in performance analysis and tuning of AI/HPC workloads.
Candidates with practical experience in GPU optimization in fields such as deep learning, computer vision, and scientific computing are preferred.
Plus:
Experience in advanced research or development in the field of High-Performance Computing (HPC) or related areas.
In-depth understanding of GPU optimization in machine learning frameworks such as Tensor Flow or Py Torch.
Practical experience in deploying and optimizing AI models using Orin and Thor platforms, as well as in developing algorithms related to autonomous driving.
Practical experience in NVIDIA GPU and CUDA programming.
More about We Ride:
Website:
Youtube: Ride AI
Linked In:
Twitter:

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