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We are doing research on deep learning research related to memory that would be suitable to extend the knowledge base of neural network for personalization, lifelong learning and handling large contexts. The team covers a broad range of topics, including LLM, text-time training, memory controller, indexing structures.
Job Responsibility:
Conduct research to advance the state of the art in architecture and memory related topics
Consistently and sustainably advance the state of the art for your problem, including setting and executing against roadmaps for 6-month plus timeframes
Collaborate with different cross-functional teams across the globe in research and product
Requirements:
PhD in Computer Science or a related field with published projects in the fields of machine learning, deep learning, robotics, large language models and/or computer vision
Proven development skills in Deep Learning, working with PyTorch or TensorFlow
Experience developing LLM algorithms or infrastructure in Python or C/C++
Nice to have:
Significant contributions to impactful work such as open-source models
Publications at peer-reviewed conferences, e.g. ICLR, ICML, NeurIPS