We’re on a mission to understand and structure the world’s medical data, starting by making sense of the terabytes of clinician notes contained within the electronic health records of the world’s largest health systems. We have raised a seed round from two top institutional funds, as well as from founders and early team members of top startups including AirBnB, Google, and athenahealth.
We’re seeking for exceptional Machine Learning Engineers to join our team, someone who can not only design machine-based systems, but also think creatively about the human interactions necessary to augment and train those systems.
*As an ML Engineer, you will:*
- Develop NLP systems that help us structure and understand biomedical information
- Work with a range of structured and unstructured data sources
- Design and build customized, large-scale cloud-based machine learning systems
- Design innovative data-acquisition and labeling systems, leveraging tools & techniques like crowdsourcing and novel active learning approaches
*We’re looking for teammates who bring:*
- Industry or academic experience working on a range of ML problems, particularly NLP
- Expert software development skills with a focus for building sound and scalable ML.
- Excitement about taking cutting-edge technologies and techniques to one of the most important and most archaic industries.
- A passion for finding, analyzing, and incorporating the latest research directly into the production environment.
- Good intuition for understanding what good research looks like, and where we should focus effort to maximize outcomes
*Bonus points if you have experience with:*
- Deep learning frameworks like TensorFlow or Theano
- Open-source NLP toolkits like Stanford CoreNLP or OpenNLP
- Developing and improving core NLP components--not just grabbing things off the shelf
- Managing large-scale crowd-sourcing data labelling and acquisition (Amazon Turk, Crowdflower, etc.)
Deep learning to read, structure, and understand electronic health records.