Machine Learning Engineer

Here’s a small sample of some of the hard tasks we solve to accomplish our goal of developing life-saving treatments for patients:

• Developing new machine learning algorithms and architectures to model complex biological systems. 
• Translating state-of-the-art machine learning techniques designed to work on images, text, or audio into the domain of molecules. 
• Creating a large-scale distributed training and hyperparameter optimization system. 
• Using data mining and processing techniques to uncover new sources of data and clean our existing datasets. 
• Using predictive modeling to make critical decisions about which tests to run in the lab.

Requirements:

• Deep Learning: Experience building CNNs, RNNs, etc. from scratch in a modern deep learning framework (Tensorflow, PyTorch, Caffe, etc.). 
• Classical ML: Experience with non-neural network deep learning models (random forests, SVM, etc.) and basic statistics. 
• Data Engineering: Experience building large-scale data processing pipelines feeding into and analyzing results from ML. 
• Experience with software development in Python. 
• BS/MS/PhD in Computer Science or a related field, or strong machine learning experience.