In our case, we're applying deep learning to sensor data, so much of the day-to-day work of a machine learning engineer is experimenting with new neural architectures rather than feature engineering by hand. For example, we're using or interested in techniques like:

  * semi-supervised sequence learning (we have a paper in a NIPS workshop next week on applying sequence autoencoders to health data, for example)

  * deep generative models

  * variational RNNs

From a day-to-day perspective, we use tools like Tensorflow and Keras, similar to most AI research labs. In general, we try to act as a software startup that happens to work in healthcare, rather than as what you might think of as a traditional biotech or medical device startup.

Does that help answer your question?