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:

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  You will develop NLP systems that help us structure and understand biomedical information and patient records
  


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  You will work with a variety of structured and unstructured data sources
  


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


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  You will work with the latest NLP approaches (BERT, Transformer)
  


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  You will train your models at scale (Horovod, Nvidia v100s)
  


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  You will use and iterate on scalable and novel machine learning pipelines (Airflow on Kubernetes)
  


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  You will read and integrate state of the art techniques into our ML infrastructure such as Mixed Precision on Transformer networks
  

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  3+ years of development experience in a company/production setting
  


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  Experience with deep learning frameworks like TensorFlow or PyTorch
  


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  Industry or academic experience working on a range of ML problems, particularly NLP
  


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  Strong software development skills, with a focus on building sound and scalable ML
  


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  Excitement about taking ground-breaking technologies and techniques to one of the most important and most archaic industries
  


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  A real passion for finding, analyzing, and incorporating the latest research directly into a production environment
  


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  Good intuition for understanding what good research looks like, and where we should focus effort to maximize outcomes
  


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  Bonus points if you have experience with:
  


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


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