Verily, an Alphabet company, lives at the intersection of technology, data science and healthcare. Our mission is to make the world's health data useful so that people enjoy longer and healthier lives. We are developing tools and devices to collect, organize and activate health data, and creating interventions to prevent and manage disease.
You will be part of a broader computational biology/data science team working on new methods and models for predicting and understanding disease using different data types. You will join this team to play a major role in applying state of the art machine learning models in image classification and segmentation to develop new diagnostic tools. As a Machine Learning Engineer on our team you will have immense opportunity to build new products, work on complex problems, and have a tremendous impact on disease diagnosis and treatment.
- Build end to end machine learning systems on large scale imaging and clinical data.
- Research novel deep model architectures, read papers, implement and deploy them.
- Build models from inception to launch: exploratory models, feature engineering, deep model architectures, tuning, and model interpretation.
- Work with latest ML techniques, including tools like TensorFlow and TPU.
- Communicate technical results with, and contribute new ideas to, a very collaborative and cross-functional team.
- Advanced degree(BS, MS or PhD) in either Computer Science or Machine Learning, or equivalent practical experience.
- Demonstrated track record, including peer-reviewed publications, of building and using deep learning models on large data, preferably images.
- Experience with Python or C++, as well as Tensorflow
- Knowledge of basic computer vision and image processing methods
- Proficient in computer programming in a scientific domain.
- Experience with exploratory and statistical data analysis (such as linear models, multivariate analysis, predictive modeling and stochastic models).
- Demonstrated ability to design and implement novel algorithms and methods.
- Experience working with large-scale imaging data, including digital pathology data.
- Experience with developing on Google's Cloud Platform.