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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.
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
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
Ways to stand out from the crowd:
Deep Learning experience
Experience with DL Frameworks (PyTorch preferred)andLarge 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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