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In 2014 Nexedi has created a technology called Wendelin.core which provides out-of-core python ndarrays that can be shared transparently across different nodes of cluster of python runtimes. With Wendelin.core, python can be used natively for big data without relying on other languages or runtimes. Wendelin.core is used in production for example to monitor wind turbines and detect anomalies.
Nexedi is looking for a trainee interested in making improving libraries utilized by our Wendelin and wendelin.core, improving the source code of NumPy and scikit-learn to reduce the number of memory allocations or copies. This task may require to modify default algorithms that use array allocations with algorithms that modify data in-place. It may also require to allocate explicitly out-of-core ndarrays whenever there is no better way. Overall, both NumPy and scikit-learn will be improved by relying as little as possible on array allocations.