Full-Time

Machine Learning Engineer / Scientist

Posted on 4/11/2024

Q Bio

Q Bio

11-50 employees

Develops whole-body autonomous scanner platform

Data & Analytics
Hardware

Mid

Remote in USA

Required Skills
Python
Tensorflow
Data Structures & Algorithms
Keras
Pytorch
Computer Vision
Requirements
  • PhD in Electrical Engineering, Biomedical Engineering, or equivalent research experience
  • At least 3 years of experience in ML model development, particularly in medical imaging utilizing MR or CT data.
  • Strong background in developing deep learning, computer vision and machine learning algorithms in Python and working with DL libraries such as TensorFlow and PyTorch, Keras, Caffe.
  • Proven experience with developing models for segmentation, classification and detection.
  • Experience working with cross-functional stakeholders including executives, scientists, clinicians, and engineers
Responsibilities
  • Explore and implement novel algorithms for medical image analysis.
  • Analyze and preprocess imaging data for model training and validation.
  • Plan and execute ML experiments, and set milestones and timelines to achieve objectives.
  • Write production level code and deploy ML-powered applications to obtain valuable insight from real life data.

Q Bio is pioneering in the healthcare technology sector with its development of the Q Bio Gemini, a novel whole-body Digital Twin platform complemented by the Q Bio Mark I, the first fully autonomous scanner. This scanner notably enhances diagnostic precision and patient comfort by completing scans in just 10 minutes without radiation or claustrophobia-inducing environments. Such cutting-edge technology not only positions the company at the forefront of medical innovation but also fosters a dynamic work environment where technical expertise and patient-centered innovation thrive.

Company Stage

Series B

Total Funding

$76M

Headquarters

San Carlos, California

Founded

2015

Growth & Insights
Headcount

6 month growth

8%

1 year growth

-2%

2 year growth

28%