Intern, ML Algorithms

Summary: Futurewei Technologies, Inc. is seeking interns with a solid background in Computer Science, Machine Learning, Statistics, and Mathematics. The role involves exploring research directions in various domains related to machine learning and statistical learning, and contributing to innovative research and publication efforts. Responsibilities: - Work with the machine learning (ML) team within our IC-Lab to investigate, explore, develop and publish research results in machine and statistical learning in one of our focused or neighboring areas - Investigate and explore cutting edge problems in domains such as long sequence processing and NLP, automatic design space exploration, reinforcement learning, computer vision, and multi-modal representation learning - Help advance the theory and practice of machine and statistical learning and application through innovative research - Publish results in top machine learning conferences and/or journals Required Qualifications: - Solid Computer Science background - Solid Machine Learning background - Solid Statistics background - Solid Mathematics background - Ph.D. student in Computer Science, Machine/Statistical Learning, Computer Engineering, or closely related majors - Well-organized - Detail-oriented - Passionate about learning - Motivated - Team player - Verbal communication skills - Written communication skills Preferred Qualifications: - Knowledge of (deep) reinforcement learning, optimization, and search techniques - Knowledge of statistical learning—e.g., deep neural networks, sequence processing, graph neural networks, etc - Familiarity with ML life cycle, architectures, and model designs - Familiarity with ML implementation environments and platforms such as PyTorch and/or Tensorflow - Familiarity with distributed system processing, Linux and Python environments, source code management, and team development practices - Familiarity with large language models (LLMs) and Visual Language Models (VLMs) Required Skills: Machine Learning, Statistical Learning, Deep Reinforcement Learning, Optimization, Search Techniques, Deep Neural Networks, Sequence Processing, Graph Neural Networks, ML Life Cycle, ML Architectures, Model Design, PyTorch, TensorFlow, Distributed System Processing, Linux, Python, Source Code Management, Passionate about learning, Motivated