Item — HN Reader

I agree--the impact of a great engineer working in healthcare is very high, particularly if you partner with medical experts.

We're a small startup that has partnered with UCSF Cardiology to detect abnormal heart rhythms, and other conditions, using deep learning on Apple Watch heart rate data:

  https://wsj.com/articles/new-study-seeks-to-use-deep-learning-to-detect-heart-disease-1458240739

  https://blog.cardiogr.am/three-challenges-for-artificial-intelligence-in-medicine-dfb9993ae750

  https://a16z.com/2016/10/20/cardiogram/

We have about 10B sensor data points so far. If you're a machine learning engineer and interested in working on this type of problem, feel free to email me: brandon@cardiogr.am.

1 comments

I'm curious. What is your definition of "machine learning engineer"? Are you talking mostly feature engineering or something deeper. If so, what?

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?