Job Description

We are a fast-paced, well funded healthtech startup breaking down the barriers to affordable and accessible healthcare. Come join us if a relaxed yet challenging startup environment drives you Candi We are a fast-paced, well funded healthtech startup breaking down the barriers to affordable and accessible healthcare. Come join us if a relaxed yet challenging startup environment drives you Candidate should possess a BS or MS degree in Biomedical Engineering, Electrical Engineering, Computer Engineering, or other related area required. Masters is preferred. . At least 2 years of experience in medical image/video processing and data visualization . In-depth knowledge of Python and C/C++ . Experience in Machine Learning frameworks (e.g., Tensorflow, Sci-Kit Learn, KERAS, Pytorch etc.). Familiar with deep learning models- CNN, LSTM, Transformers etc. . Familiar with computer vision and medical signal processing techniques using Numpy and Pandas. Hands-on experience with both classical machine learning methods (regression and classification) and neural networks . Understanding of statistical methods such as significance testing for multiple comparison of different methods and visualization of results for scientific reporting . Solid understanding of how to use tools like GitHub, Git, CI/CD and Jira Roles & Responsibilities: (Responsible for signal processing, computer vision, ML algorithms) . Design and prepare clinical, laboratory and in-house testing studies for feasibility, development, and validation . Design, iterate, and optimize Nervotec's signal processing, computer vision, and machine learning algorithms to achieve best in class computational efficiency and clinical performance . Build pipelines and automation for processing, data analysis, and machine learning model training to accelerate algorithm development . Document how algorithms have been designed, implemented, verified, and validated . Contribute to Nervotec's intellectual property and research publications Technical Skills Requirements: Proficient in: . Python and C/C++ . Machine Learning Frame works: Tensorflow, Sci-Kit Learn, KERAS, Pytorch . Familiar with deep learning models- CNN, LSTM, Transformers etc. . Classical Machine Learning methods: Regression and Classification . Open Computer Vision (OpenCV) . GitHub, Git, CI/CD and Jira