Join DeepScribe and be a part of the next technological transition in health care - voice. Our goal is to empower physicians with the tools they need to improve both efficiency and efficacy, and better patient outcomes by increasing the clarity, trust, and understanding they have with their physician.

Our first product, an AI medical scribe, mimics near-human level intelligence by parsing through medical conversations and creating detailed visit summaries for physicians. With this ambient scribe technology, we have been able to save up to 3 hours a day for physicians. But this is just the beginning - come join our energetic, fast-moving team as challenge the status quo in the bogged-down health care industry, and re-think the future of practicing and receiving medicine.

Job Description

At DeepScribe, we’re looking for an NLP Engineer that’s as excited about pushing the field of machine learning forward as they are shipping their code to production.

Our state-of-the-art models are ingesting and generating summaries for thousands of real-life patient-physician interactions weekly. You’d be working directly with our current models to tune them to the abundant inflow of data as well as architecting new ones to further infuse AI into physican workflows.

You’ll be an integral part of DeepScribe, working on the core tech that makes our product what it is today. It doesn’t stop there though… As we collect more and more rich conversational data, get ready to shape the future of data-driven care.

Requirements

  • BS, MS, PhD in Computer Science, Data Science, Statistics or related discipline, or equivalent industry experience
  • Strong foundation in Python, C++, or similar
  • Experience with common ML frameworks such as PyTorch or Tensorflow
  • Understanding of the trade-offs between machine learning models on NLP tasks
  • Experience working with large unstructured text datasets
  • Fundamental understanding of foundation NLP techniques (LSTMs, NER, Word Embeddings, etc)

Responsibilities

  • Modifying and optimizing our current NLP models
  • Tune models to the constant inflow of conversational data
  • Prototyping and deploying new models for new applications
  • Data structuring and cleaning
  • Working with our infrastructure team to get your models into production
  • Demoing your awesome work to the leadership team