Machine Learning Engineer

Job DescriptionCompany Description PraecisAI Corp. is a groundbreaking technology company focused on applying advanced machine learning and artificial intelligence to bring advanced cardiac diagnostics and care to rural and remote communities where there is none. Our proprietary physiological vector technology brings prediction analysis to allow detection of cardiac disease well before symptoms appear. Heart disease is the number one cause of death globally and our platform will save lives and prevent disability making global communities healthier and prevent loss of quality of life and productivity from cardiac disability. Our flagship product CardioSense will disrupt medicine everywhere making it predictive rather than reactive. We have major collaborations with several top universities in San Francisco, London, Berne, and Manila. We seek a motivated, curiosity driven person who thrives in an interactive group and brings the highest level of inquitiveness and technical expertise. Be part of our paradigm shifting approach and solving the most important problem in global health.\n\nRole Description This is a part-time, co-founding Machine Learning Scientist on designing, building, and developing and deploying machine learning models on medical data. Responsibilities include developing and optimizing algorithms, implementing and training neural networks, and performing pattern recognition and statistical analysis on structured and unstructured data. The role involves writing clean, efficient code, conducting experiments, evaluating model performance, and iterating on models based on quantitative metrics. The Machine Learning Scientist will collaborate with our computer and electrical engineers and biostatisticians to support the integration of models into existing systems and pipelines.\n\nWe are unable to provide H-1 or other visas st this time and apologize to our foreign scientific colleagues.\n\nQualifications\n\n\n Strong foundation in Machine Learning, Computer Science and Algorithms, with practical experience implementing efficient, scalable solutions.\n Applied knowledge of Statistics for experimental design, hypothesis testing, and performance evaluation of machine learning models.\n Hands-on experience with machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn) and Python.\n Familiarity with data preprocessing, feature engineering, and working with real-world electronic datasets.\n Ability to work independently in a remote setting, and communicate clearly.\n Masters degree or higher in Machine Learning with fluency in Computer Science, Data Science, and Mathematics,\n