Baidu Research Institute of Deep Learning (http://idl.baidu.com), Silicon Valley, is looking for research scientists with strong background in machine learning, natural language processing, and computer vision. Our mission is to build next generation technologies for better connecting billions of users to services. As a research scientist at Baidu, you will be uniquely positioned in our team to work on very large scale of industry problems and to push forward frontiers of AI technologies.
Baidu Institute of Deep Learning has an agile team of scientists and experienced machine learning practitioners. We have been building the largest deep learning platform and have many successes in applying advanced machine learning technologies to build better services and software. Successful products include image search, OCR, visual product search, face recognition, Ads, anti-virus, etc.
At IDL, we will provide:
- An environment for you to learn cutting edge technologies in machine learning, deep learning, NLP, computer vision, robotics, and HCI.
- Opportunities to learn state-of-the-art industry application of machine learning technologies at massive scale.
- Opportunities to access to huge application-specific proprietary data.
- Guidance from a team of world-renowned leaders in machine learning, statistics, computer vision, HCI, robotics, etc.
Here is what we’d like to see in you:
- Strong analytical skills (probability, algebra, optimization, etc.).
- Ability to come up with practical algorithms and write solid code quickly in a programming language (such as C++, Java, Python, GPU CUDA programming).
- Hungry to learn new things.
- Team orientation: we work towards our goal as an agile team together, rather than individually.
Experience in one of the following (but not limited to) is preferred:
- Machine learning: deep learning, large-scale optimization, probabilistic inference, etc.
- Natural language processing: sequence segmentation, labeling and parsing, language modeling, machine translation, question answering and dialog systems, knowledge extraction, representation and reasoning, multi-modal (e.g. image-text) learning, etc.
- Computer vision: large scale image classification, detection, segmentation, OCR, face recognition, scene understanding, metric learning, image search, etc.
- Reinforcement learning: POMDP, imitation learning, Monte Carlo search, etc.