Artificial Intelligence Intern
We are looking for a new Artificial Intelligence Intern (Artificial intelligence for IT Operations). The role of the intern is to conduct technical research on different machine learning algorithms to detect IO patterns and improve the cache pre-fetch. The intern will also have the opportunity to work as the strategic thought leader, to educate the team in distributed storage system, cloud storage and big data.
Key Roles and Responsibilities:
Research and develop machine learning algorithms for IO pattern detection in real time
Run experiments to measure algorithms’ effectiveness and performance
Optimize algorithms for real-time performance
Write documentation and tests for code
Work in a team environment by following Agile software development process
Create and develop new ideas in a functional role of research
Engage with domain experts as well as senior leaders and engineers to effectively deliver interpretative results
Deal with enormous amounts of unstructured data, and developing solutions in a distributed setting
Present and publish your findings at top-tier conferences and journals
Currently enrolled in an MS or PhD program in Artificial Intelligence, Computer Science, Information technology, or related field
Experience in developing software system with machine learning services
Experience programming in Python and C
Experience in development on cloud platform
To be currently working towards an MS or PhD in Machine Learning, Computer Vision or related (e.g., Statistics, Mathematics, Physics, Operations Research, Computer Science) – outstanding undergraduate candidates will be considered as well with a shown track record in the above fields
Substantial real-world experience with unstructured data sources, especially in areas of spatiotemporal data and/or vision
Expertise in predictive analytics/statistical modeling/data mining algorithms
Knowledge/experience in some/all of the following: Multivariate Regression, Logistic Regression, Support Vector Machines, Bagging, Boosting, Decision Trees, common clustering algorithms, Optimization, Stochastic Processes, Recommendation Systems, Metric Learning, Multi-Task/Transfer Learning, Active Learning, Hierarchical/Deep Learning.
Interest/commitment to working in a startup environment.