NVIDIA is looking for engineering and research interns to join its Deep Learning algorithms team. Academic and commercial groups around the world are using GPUs to revolutionize deep learning and data analytics, and to power data centers. Join the team building software which will be used by the entire world. Interact with the scientific community to implement the latest algorithms. Ability to work on a dynamic customer-oriented team is required and excellent interpersonal skills are also a requirement.

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In this role you will be interacting with internal partners, users, and members of the open source community to analyze, define and implement highly optimized algorithms and DL frameworks. The scope of these efforts includes a combination of performance tuning and analysis, defining APIs, analyzing functionality coverage, implementing new algorithms and frameworks, and other general software engineering work.

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What you’ll be doing:

• Research, analyze, and document state-of-the art algorithms
• Design and implement a deep learning framework for model optimization
• Develop algorithms for deep learning, data analytics, machine learning, or scientific computing
• Analyze performance of GPU implementations
• Benchmark software stacks across training and inference scenarios
• Evaluate and understand capabilities of frontier models
• Collaborate with team members and other partners

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What we need to see:

• Pursuing MSc or PhD in Computer Science, Artificial Intelligence, Applied Math, or related field
• Excellent programming in Python, debugging, performance analysis, and test design skills
• Strong algorithms and mathematical fundamentals
• Good understanding of Deep Learning fundamentals
• Ability to work independently and manage your own development effort
• Good communication and documentation habits

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Ways to stand out from the crowd:

• Deep Learning experience
• Experience with DL Frameworks (PyTorch preferred) and  Large Language Models
• Experience with model compression techniques such as pruning, NAS, distillation, and quantization
• Knowledge of CPU and/or GPU architecture
• First-author publication in a top-tier deep learning or AI conference

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