Nvidia AI Algorithms Software Engineer RDSS Intern 

Taiwan, Taiwan Province, Hsinchu 

122152019

This challenging role will require someone who deeply understands and can architect algorithms with Large Language and Multi-modal (LLM/LMM) Foundation models to advance the application of artificial intelligence and machine learning to the Manufacturing AI market. Practical experience in the use and the building of Computer Vision algorithms, models and tools will be critical.

What You’ll Be Doing:

• You will be a key member of a growing software team that can architect, analyze, develop and prototype key deep learning algorithms and solutions.

• Work and collaborate with different software, research, and hardware teams across geographies for solving critical problems.

• Develop algorithms, such as zero/few-shot learning and unsupervised learning, to address challenges related to data scarcity and collection.

• Optimize deep learning models for deployment using techniques like model distillation, quantization, pruning, and others to ensure the highest efficiency across platforms

• Understand and analyze the interplay of hardware and software architectures on future applications.

• Support engagements with customers and their third-party software providers, collaboration with Product Managements, Marketing, and Developer Technology teams.

What We Need To See:

• MS or PhD candidate graduating in 2025, with expertise in Computer Science, Computer Engineering, Electrical Engineering, or a related field with a focus on Deep Learning, Machine Learning, and Computer Vision.

• Strong algorithm development experience, particularly in data analytics, with a focus on large language models (LLMs) and multi-modal foundation models.

• Experience with advanced algorithms, including zero/few-shot learning, self-supervised and unsupervised learning, as well as domain adaptation methods like PEFT (Parameter-Efficient Fine-Tuning) and generative AI models for synthetic data creation.

• Expertise in deep learning model optimization techniques, such as model distillation, quantization, pruning, and other methods that improve computational efficiency..

• Hands-on experience with deep learning frameworks like TensorFlow and PyTorch.

• Strong communication skills.