Software Engineer
Machine Learning
Confirmed live in the last 24 hours
Fathom Health

51-200 employees

AI-powered autonomous medical coding for healthcare efficiency
Company Overview
Fathom is a pioneering company in the healthcare sector, utilizing advanced deep learning and natural language processing to autonomously code patient encounters, offering the highest automation rates and broadest specialty coverage in the industry. With the capacity to code millions of charts per day, Fathom can reduce coding operation costs by up to 50%, significantly shorten coding turnaround times, and provide on-demand coding capacity, all while ensuring patient information security. Backed by prominent investors and healthcare leaders, Fathom's technology is a testament to its industry leadership and commitment to enhancing efficiency in healthcare systems globally.
AI & Machine Learning

Company Stage

Series B

Total Funding

$65.9M

Founded

2017

Headquarters

San Francisco, California

Growth & Insights
Headcount

6 month growth

26%

1 year growth

77%

2 year growth

142%
Locations
Toronto, ON, Canada
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Kubernetes
Tensorflow
Data Structures & Algorithms
Pytorch
Natural Language Processing (NLP)
CategoriesNew
AI & Machine Learning
Requirements
  • 2+ 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
  • A desire to collaborate in office 3 days weekly
Responsibilities
  • 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
Desired Qualifications
  • 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.)