AI/ML Job: Software Engineer, Machine Learning

Fathom Health

Software Engineer, Machine Learning at Fathom Health

Toronto, Canada 🇨🇦   (Posted Jul 23 2018)
About the company
Fathom Health is a deep learning NLP system to accelerate medical reimbursement, backed by world class investors including Google Ventures, 8VC, and Stanford, as well as founders and early employees from companies like Google, Dropbox, Airbnb, and athenahealth.

Job position

Job description
Are you passionate about machine learning and looking for an opportunity to make an impact in healthcare?

Fathom is 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 health systems.

We are seeking extraordinary Machine Learning Engineers to join our team, developers and scientists who can not only design machine-based systems, but also think creatively about the human interactions necessary to augment and train those systems.

As a Machine Learning Engineer you will:

Develop NLP systems that help us structure and understand biomedical information and patient data

Work with a variety of structured and unstructured data sources

Design and build customized, large-scale cloud-based machine learning systems

Imagine and implement creative data-acquisition and labeling systems, using tools & techniques like crowdsourcing and novel active learning approaches

Skills & requirements
We’re looking for teammates who bring:

Experience with deep learning frameworks like TensorFlow or PyTorch

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:

Developing and improving core NLP components—not just grabbing things off the shelf

Leading large-scale crowd-sourcing data labelling and acquisition (Amazon Turk, Crowdflower, etc.)

Instructions how to apply
see the website
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