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Senior Software Engineer (Machine Learning)

Fathom logo

Location
United States
Base Salary
175k-220k USD
Fathom

Job Description

Fathom is on a mission to use AI 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. Our deep learning engine automates the translation of patient records into the billing codes used for healthcare provider reimbursement, a process today that costs hospitals in the US $15B+ annually and tens of billions more in errors and denied claims. We are a venture-backed company that completed a Series B round of financing for $46M in late 2022.
Are you passionate about machine learning and looking for an opportunity to make an impact in healthcare? We are seeking an extraordinary Senior Software Engineer (Machine Learning) based remotely in North America, who can not only design machine-based systems, but also think creatively about the human interactions necessary to augment and train those systems. We want to work with teammates excited about taking ground-breaking technologies and techniques to apply to one of the most important and most archaic industries. This is an opportunity to join our team and 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.

Please note that this position requires physical residency in the U.S.

Your role and responsibilities will include:

  • Developing NLP systems that help us structure and understand biomedical information and patient records
  • Using a variety of structured and unstructured data sources
  • Imagining and implementing creative data-acquisition and labeling systems, using tools and techniques like crowdsourcing and novel active learning approaches
  • Working with the latest NLP approaches (BERT, Transformer)
  • Training your models at scale (Horovod, Nvidia v100s)
  • Employing and iterating on scalable and novel machine learning pipelines (Airflow on Kubernetes)
  • Reading and integrating state of the art techniques into Fathom’s ML infrastructure such as Mixed Precision on Transformer networks

We are looking for a teammate with:

  • 5+ years of software engineering experience in a company/production setting
  • Knowledge of algorithms, data structures and systems design, with a focus on building sound and scalable ML
  • Experience with deep learning frameworks like TensorFlow or PyTorch
  • Industry or academic experience working on a range of ML problems, particularly NLP
  • A real passion for finding, analyzing, and incorporating the latest research, technologies and techniques 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:

  • Developed and improved core NLP components and not by just 'grabbing things off the shelf'
  • Led large-scale crowd-sourcing data labeling and acquisition (Amazon Turk, Crowdflower, etc.)

Compensation:

  • Salary: $175,000 USD - $220,000 USD
  • Company Equity

Benefits:

  • Unlimited Vacation + Sick Days
  • Medical/Dental/Vision Coverage
  • 401k

Advice from our career coach

As a Senior Software Engineer (Machine Learning) at Fathom, you will be leading the development of NLP systems to structure and understand biomedical information and patient records. To stand out as an applicant for this role, here are some tips:

  • Highlight your experience with deep learning frameworks like TensorFlow or PyTorch.
  • Showcase your industry or academic experience working on ML problems, especially in NLP.
  • Demonstrate your ability to integrate the latest research and techniques into a production environment.
  • Emphasize your knowledge of algorithms, data structures, and systems design for building scalable ML solutions.
  • If applicable, mention any experience in large-scale crowd-sourcing data labeling and acquisition.

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