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 an extraordinary Software Engineer, Machine Learning 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 records
  • Work with a variety of structured and unstructured data sources
  • Imagine and implement creative data-acquisition and labeling systems, using tools & techniques like crowdsourcing and novel active learning approaches
  • Work with the latest NLP approaches (BERT, Transformer)
  • Train your models at scale (Horovod, Nvidia v100s)
  • Use and iterate on scalable and novel machine learning pipelines (Airflow on Kubernetes)
  • Read and integrate state of the art techniques into Fathom’s ML infrastructure such as Mixed Precision on Transformer networks.


We’re looking for teammates who bring:

  • 2+ years of development experience in a company/production setting
  • Experience with deep learning frameworks like TensorFlow or PyTorch
  • Industry or academic experience working on a range of ML problems, particularly NLP
  • Strong 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 a 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 labeling and acquisition (Amazon Turk, Crowdflower, etc.)

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