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Full-Time

Machine Learning Research Scientist

Confirmed live in the last 24 hours

Rad AI

Rad AI

51-200 employees

AI-driven software for radiology workflows

Hardware
AI & Machine Learning
Biotechnology
Healthcare

Senior, Expert

Remote in USA

Category
Deep Learning
Natural Language Processing (NLP)
AI & Machine Learning
Lab & Research
Interdisciplinary Research
Required Skills
Python
Pytorch
Natural Language Processing (NLP)
Requirements
  • Preferred PhD in CS or related field
  • Experience developing and testing NLP models in a commercial or academic setting
  • Experience applying ML solutions to real world problems
  • Experience with deep learning frameworks (e.g. PyTorch) and common NLP frameworks such as HuggingFace
  • Extensive programming experience, with a focus on Python
Responsibilities
  • Develop Advanced Deep Learning Models: Harness the power of deep learning to create sophisticated models capable of performing a range of reasoning and generative tasks, specifically tailored to the nuances of clinical data
  • Innovate in Clinical Problem-Solving: Apply your expertise to solve intricate clinical challenges, contributing directly to real-world healthcare advancements
  • Collaborative Solution Development: Engage actively with both technical peers and clinical experts to innovate and refine solutions that have a tangible impact in the field of healthcare
  • Architect Interdisciplinary Solutions: Play a pivotal role in designing solutions that bridge multiple disciplines. Determine the optimal integration points for machine learning in these solutions and conduct objective evaluations of competing methodologies
  • Research Integration and Experimentation: Stay at the forefront of machine learning advancements by reviewing academic papers, designing experiments, and integrating cutting-edge research into our platform
  • Mentorship and Collaboration Opportunities: Contribute to the growth of our dynamic Machine Learning Team through mentorship roles and collaborate with our esteemed in-house team of leading clinicians

Rad AI enhances radiology workflows using artificial intelligence to improve efficiency and accuracy in radiological practices. Its main product, Omni Reporting, automates routine tasks, ensures follow-up on incidental findings, and enhances reporting accuracy. This software integrates seamlessly into existing workflows, making it easier for radiologists to manage their work. Unlike competitors, Rad AI emphasizes data security and patient privacy, being SOC 2 Type II and HIPAA certified. The company operates on a subscription-based model, providing continuous updates and improvements to its software. The goal of Rad AI is to improve patient outcomes and streamline healthcare processes in the radiology field.

Company Stage

Series B

Total Funding

$89.8M

Headquarters

San Francisco, California

Founded

2018

Growth & Insights
Headcount

6 month growth

22%

1 year growth

51%

2 year growth

114%
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Simplify's Take

What believers are saying

  • Rad AI's recent $50M Series B funding, led by Khosla Ventures, positions the company for significant growth and expansion in the AI healthcare market.
  • The company's technology is already used by about a third of U.S. health systems, indicating strong market penetration and customer trust.
  • Innovative features like Omni Unchanged demonstrate Rad AI's commitment to reducing radiologists' workload and improving efficiency, which can lead to higher job satisfaction and better patient outcomes.

What critics are saying

  • The rapid pace of technological advancements in AI and healthcare could render Rad AI's solutions obsolete if the company fails to innovate continuously.
  • The competitive landscape in AI-driven radiology is intensifying, with new entrants and existing players potentially eroding Rad AI's market share.

What makes Rad AI unique

  • Rad AI's Omni Reporting software, recognized as the Best New Radiology Software by AuntMinnie, sets it apart in the radiology AI market.
  • The company's strong emphasis on data security and patient privacy, evidenced by SOC 2 Type II and HIPAA certifications, provides a competitive edge in the healthcare sector.
  • Rad AI's early adoption of generative AI and proprietary LLMs for radiology report automation distinguishes it from competitors who are only now exploring these technologies.