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Expired Vacancy
Machine Learning Engineer

, , Machine Learning, Software Engineering,
   Location: Canada, Toronto
   Salary: On application
Experience: High
Deadline: 01 May 2018
  Organisation: Fathom Health




Please note that this vacancy has expired and you can no longer apply.

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

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 on building sound and scalable ML.
      • Excitement about taking ground-breaking technologies and techniques to one of the most important and most archaic industries.
      • A real 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
    • Leading large-scale crowd-sourcing data labelling and acquisition (Amazon Turk, Crowdflower, etc.)

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