Machine Learning Engineer

In both of these cases of artificial intelligence, the evolution and performance of the algorithms result from learning based on data.

Machine learning (ML) is the application of statistical methods to computers to improve their performance in solving tasks without being explicitly programmed. The performance of a machine learning model is linked to the quality of the training data and the compatibility of the chosen model. Machine learning algorithms cover both "classical" algorithms and neural networks. Deep learning (DL) is limited solely to learning through neural networks. It is particularly well suited to processing unstructured data such as images or text and extracting relevant information from it (pattern recognition, automatic speech recognition, automatic natural language processing, etc.).

Some engineers practice both technologies, while others specialize in ML or DL. It is also possible to focus on specific applications or to take on a more project management-oriented role as a machine learning project manager or deep learning project manager. In both cases, engineers apply the latest innovations from the R&D team in terms of algorithms. They develop and test them using one of the artificial intelligence algorithms.

Machine learning engineer

Their main role is to select, train, and deploy learning models based on a dataset. They may also develop algorithms and write programs to extract relevant information that will be used in the modeling phase. The goal is to enable computers to respond to complex problems. This work lies at the intersection of science, computer science, and mathematics. This specialist may also be in charge of data engineering, which ensures that all data is clean. Often confused with data analysts—who specialize in a category of data related to a business or strategic issue—data scientists have a more global vision and a more cross-functional role.

Deep learning engineer

The role of a deep learning engineer is to be a specialist in the development and implementation of learning algorithms based on deep and complex neural network architectures. This is a more technical task than that of a "traditional" machine learning engineer, as the tools used are more advanced from a theoretical point of view. In agriculture, for example, deep learning enables equipment to differentiate between plants and distribute the appropriate treatment to them, reducing the use of herbicides and improving production. The system is based on visual recognition. Deep learning includes convolutional neural networks (mainly suited to image recognition) and recurrent neural networks (effective for time series problems), among others.