AI Engineer (Chaos Research Institute)
The purpose of the Chaos Institute is to provide background information and tools to help people solve the difficult problems they experience in their daily lives. There’s a reason why these problems are so challenging: we’ve been trained to think in ways that aren’t very compatible with complexity. New ways of thinking are being developed that are much more useful for dealing with complexity at all levels of scale, from family issues to international disputes. On this website you’ll learn a bit about how our thinking has been shaped by science, and how the new sciences of chaos and complexity theories, along with systems thinking, are generating approaches that are more effective in handling such issues. If you use the information on this site and make an effort to learn about complexity, you’ll find yourself developing new capacities for problem-solving.
The Chaos Institute was created by Jo Vanderkloot and Judy Kirmmse: we are two friends and collaborators who share an interest in solving complex problems using tools derived from these models. Separately and together we have honed this approach working with families and workplaces. We are eager to share what we’ve learned and to dialogue on our blog about difficult problems our readers are facing. Here is our background.
- Participate in AI brain project development and research, have a certain understanding of server architecture.
- Participate in the feasibility analysis of brain project, including architecture, feature extraction, algorithm (NASH equilibrium, CFR), regression model.
- Familiar with HMM, GMM, and joint probability decision making model.
- Transform your research interest and enthusiasm into AI brain with real commercial value.
Qualifications
- Masters degree or above. Bachelor’s degree might also be acceptable with excellent work.
- Responsible for developing and researching the core technology of machine learning.
- Familiar with the advantages and disadvantages of all kinds of deep neural base network, and can optimize the parameter model and parameter deployment.
- Innovate the model and design the appropriate algorithm according to the application scenarios, to improve the accuracy of the algorithm.
- Familiar with HMM, BOOSTING, BAGGING, SVM, LASSO, PCA and other machine learning algorithms.
- Familiar with various regression models, and the advantages and disadvantages of various regressions, as well as the use scenarios of various regression models.
- Familiarity with linear algebra, dual problems, Lagrange's theorem, Bayesian probability theory.
- Proficient in Python, C, C++, CUDA; understand NEON.
- Familiar with AI development platform, including TensorFlow, Caffe, Caffe2, pytorch, torch, CNTK, etc.
- Independent model designs are preferred or have published articles in top conferences/journals, or shared contributions from open sources, or ranked among the top performers in related competitions.
To apply, please submit your resume to resume@alariss.com and note why you believe you'd be a good fit.