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We are building a team focusing on personal multi-agent systems, that will go out in the world and act on our behalf. The team covers a broad range of topics, including cooperative and social LLM agents, multi-agent planning and reasoning, self-improvement and evolutionary techniques, multi-agent reinforcement learning, human-agent collaboration, simulation environments and tool use.
Job Responsibility:
Conduct applied research to advance the state of the art in Language / Multimodal Models and Agentic systems
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++
First-authored publications at peer-reviewed conferences, e.g. ICLR, ICML, CVPR, ECCV, ICCV, NeurIPS, SIGGRAPH