PaxeraHealth is one of the best and brightest medical solutions companies to work for. We develop technologies that help manage millions of medical images every year. We're actively involved in shaping the industry and transforming technologies so radiologists and clinicians have what they need.
The
AI Engineer is responsible for understanding the medical imaging business problems, identifying/applying the right AI or cognitive computing technologies. In addition to being involved in the formulation and execution of technology solutions.
RESPONSIBILITIES:
- Develops required machine learning models or prototype applications, applying formulated AI recipes and verifying the problem/solution fit
- Understands the challenges in medical imaging and using state of the art solutions in AI and DL
- Participates in building new experiences, improves existing products, ensures quality assurance test designs and contributes to test systems architecture discussions
- Works alongside leading innovators in the field of deep learning and health care, plays a critical role in algorithm development
- Analyzes and evaluates data and builds deep networks to detect or predict features, while effectively balancing quality, availability, timeliness and cost factors
Job Requirements
- 2-3 years of AI/ML experience with a degree in a quantitative fields like Computer Science, data science, Engineering or related field required, with a significant focus on deep learning
- BA/BS degree in computer science or related discipline is required, MS degree is preferred
- Good command of the English language required (spoken and written)
Required Skills
- Strong background in machine learning and parallel processing pipelines, with significant experience in image processing
- Experience in working with large datasets and relational databases (SQL)
- Experience with deep learning frameworks like TensorFlow, Caffe, Torch, Theano; projects on computer vision are a strong plus
- An intuitive sense of how to architect high-performance distributed computing systems for machine learning & tie them to business problems
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