Deep-Learning Software Engineer, Performance Optimization
Description For Deep-Learning Software Engineer, Performance Optimization
NVIDIA is seeking a Deep Learning Software Engineer to join their team in Tokyo, focusing on performance optimization of deep learning solutions. This role combines cutting-edge AI development with practical implementation, requiring expertise in both theoretical machine learning and hands-on software engineering. The position involves working with NVIDIA's latest hardware platforms to optimize and deploy deep neural networks for real-world applications. As a senior member of the team, you'll collaborate with researchers and engineers across NVIDIA to improve chip architecture while also working directly with partners to implement AI solutions. The role requires strong technical skills in C++, Python, and various deep learning frameworks, along with experience in model optimization techniques. This is an opportunity to work at the intersection of hardware and AI, helping shape the future of accelerated computing at a global technology leader. The position offers exposure to the latest developments in AI technology and the chance to work on projects that will impact various industries through NVIDIA's widespread presence in the AI and GPU computing space.
Responsibilities For Deep-Learning Software Engineer, Performance Optimization
- Analyze, profile and optimize the latest DNN AI algorithms
- Implement production-quality software libraries for latency-critical use-cases
- Push boundaries in DNN performance and efficiency, including model compression and quantization
- Collaborate with researchers and engineers across NVIDIA to improve chip architecture
- Assist NVIDIA customers in implementing AI technology
Requirements For Deep-Learning Software Engineer, Performance Optimization
- University degree in Computer Science, Electrical Engineering, Physics or Mathematics
- 5+ years of work experience in HPC, numeric computing, machine learning, or AI
- Proficiency in C++, Python, data structures, algorithms
- Knowledge of deep-learning toolchains (PyTorch, TensorFlow, Keras, ONNX, TensorRT)
- Experience with neural network training, pruning and quantization
- Experience optimizing algorithms on accelerated hardware
- Familiarity with CNN, LLM and ViT architectures
- Experience creating DNN models for production problems
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