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Junior GPU Engineer

4 months ago

San Jose, United States Ampstek Full time

Junior GPU Engineer

Location: San Jose, CA (Onsite)

Min exp : 7+ Years


Job Description.

We are seeking a highly motivated and skilled GPU Resource Specialist to join our team. The ideal candidate will have a strong understanding of graphics processing units (GPUs), their architecture, and their applications across various industries. The Junior GPU Resource Specialist will be responsible for assisting in the management and optimization of GPU resources, ensuring efficient utilization and performance. This role requires a blend of technical expertise, analytical skills, and the ability to collaborate effectively with cross-functional teams.


Responsibilities:


Assist in the allocation and provisioning of GPU resources across multiple projects and departments.

Monitor GPU usage and performance metrics to identify bottlenecks and optimize resource utilization.

Collaborate with IT and engineering teams to ensure proper configuration and maintenance of GPU infrastructure.

Conduct performance analysis and tuning to maximize GPU efficiency and throughput.

Implement optimization techniques to improve rendering speeds, reduce latency, and enhance overall performance.

Work closely with software developers to identify and resolve GPU-related performance issues.

Maintain detailed documentation of GPU configurations, software installations, and performance benchmarks.

Generate regular reports on GPU utilization, performance metrics, and optimization efforts for stakeholders.

Stay updated on the latest advancements in GPU technology, including hardware innovations and software frameworks.

Experiment with new GPU features, libraries, and tools to assess their potential impact on performance and productivity.

Contribute to the development of best practices and guidelines for GPU utilization within the organization.

Participate in cross-functional initiatives to integrate GPU acceleration into software applications and workflows.

Qualifications:


Strong understanding of GPU architecture, parallel computing principles, and graphics rendering techniques.

Proficiency in GPU programming languages such as CUDA or OpenCL.

Experience with GPU profiling and performance analysis tools (e.g., NVIDIA Nsight, AMD Radeon Profiler).

Familiarity with GPU-accelerated libraries and frameworks (e.g., cuDNN, TensorRT, OpenGL, Vulkan).

Excellent analytical and problem-solving skills, with a keen attention to detail.

Effective communication and interpersonal skills, with the ability to collaborate effectively in a team environment.

Prior experience with GPU resource management or optimization is a plus.

Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) and deep learning concepts is desirable but not required.


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