Machine Learning Researcher

Key Skills

Python Programming, Scala Programming, C++, Java Programming, Machine learning techniques, Data science techniques, SQL, Apache Hadoop, MapReduce, TensorFlow, PyTorch

Job Description

• Collaborate with our university professor partners and guide world-class product development teams to transfer ideas into solutions in the next generation of Huawei consumer products.
• Use deep understanding of machine learning and artificial intelligence to define and tackle meaningful technical problems.
• Participate in local and international AI/ML academic conferences.
• Prepare technical presentations that will be shared with team and academic collaborators
• Publish regularly in top international AI conferences such as NeurIPS, ICML, ICCV, ECCV, ICML, AAAI, etc.
• Define Huawei's next-generation consumer products by collaborating with other Huawei R&D teams in computer vision lab, speech and semantics lab, cloud distributed data.
• Grasp key technology requirements and tailor detailed research topics and plans.
• Fulfill technical requirements of product development and open-source development. Your research will be applied widely in the terminal field of consumer business, such as mobile and IoT devices, smart watch, and autonomous driving solution as well as Huawei open-source projects such as MindSpore. The featured result of the research will be presented to the world via the latest Huawei Product Launch Event.

Qualifications

• Hold a PhD in Applied Mathematics or Computer Science.
• Expert in numerical optimization with proven publication record in the field. Expertise in nonlinear or blackbox optimization is preferred.
• Familiar with deep learning, proven through publications in AI/ML conferences
• Good knowledge of Deep Learning (DL) components (training, regularization, fine tuning, etc)
• Familiarity with common DL architectures (CNNs, RNNs, Attention models, etc)
• Strong coding skills in Python and C++
• Experience with coding in TensorFlow or PyTorch;
• Creative, collaborative, and innovation-focused.
• Strong teamwork and technical presentation skills
• Passionate for creating innovative techniques, and making them robust and scalable.
• Experience with Android or other mobile systems, hardware systems, software systems or operating systems is a plus.
• Experience in applying machine learning and deep learning models on industrial scale problems is a plus.
• Some background knowledge in Neural Network Compression techniques (Quantization, Pruning, Tensor decomposition, Weight sharing, ) is a plus.
• Experience in parallel model training is a plus.
• Familiarity with DL compression methods such as pruning, quantization, weight sharing, etc is a plus.