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Research Engineers(AI & Health Computing)

Source:       Time:2021-05-12

Job responsibilities:

1.Implement and develop machine learning models or deep reinforcement learning algorithms, such as deep generative models, depth map models, reinforcement learning models, and apply these technologies in products;

2.Develop models and algorithms in natural language processing, such as text classification, machine translation, reading comprehension, text summarization, text generation, etc., and algorithms related to automatic Q&A and conversation;

3.Use computer vision algorithms for image classification, image segmentation and other tasks;

4.Develop machine learning systems;

5.Develop smart information retrieval algorithms, build knowledge graphs, and help implement search, recommendation, Q&A, conversation and other applications on the ground.

Qualifications and requirements:

1.Candidates should be graduates from well-known universities with a degree in computer science, electronic information, applied mathematics, pattern recognition, artificial intelligence and other related majors;

2.Candidates should have at least three years of working experience at renowned research institutes, or leading companies, in related fields;

3.Candidates should be proficient in NLP/ML/DL models and algorithms (e.g. GBDT/MLP/CNN/RNN/LSTM/Transformer), and experience in developing large-scale pre-training models;

4.Candidates should be skilled at one or more mainstream programming framework for deep learning (Caffe, TensorFlow, PyTorch, PaddlePaddle), and familiar with their underlying architecture and implementation mechanisms;

5.Candidates should have an ideal in scientific research, show extraordinary abilities in raising questions and solving problems in doing research, have good communication and teamwork abilities, and acknowledge the AIR culture.


1.An open and committed cultural atmosphere, and a chance to work with great minds;

2.Tsinghua research strengths coupled with top-tier enterprise resources, providing abundant data and scenarios for platform technology innovation;

3.Comprehensive and convenient mechanisms for research results conversion and project incubation;

4.Better work-life balance, and freer work environment.

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