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What you will be doing:
Develop deep learning models using NVIDIA's SDK for high-performance inference
Optimize DNN performance such as accuracy, latency, memory traffic, etc.
Use C++, Python and CUDA to build graph parsers, converters, compiler, and tools for effective deployment of trained deep learning models.
Work with cross-collaborative teams of deep learning software engineers and GPU architects to innovate across applications like autonomous driving, NLP, and computer vision.
What we need to see:
Pursuing a MS or PhD in Computer Science, Electrical Engineering or related field
Knowledge of software development or Machine learning or high-performance computing
Experience in developing or using deep learning frameworks (e.g., PyTorch, JAX, TensorFlow, or ONNX, etc.)
Strong C++ programming skills including debugging, performance analysis, test design, and optimizations.
Ways to stand out from the crowd:
Familiarity with sophisticated C++11/C++14 language features and CUDA kernel programming.
Experience working in an open-source and software development on Windows.
Strong time-management and organization skills for coordinating multiple initiatives, priorities and implementations of new technology and products into very complex projects.
You will also be eligible for Intern
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