Machine Learning Engineer - Computer Vision

Company Description AngioInsight provides an AI-driven solution for real-time, non-invasive assessment of stenosis and Fractional Flow Reserve (FFR) in cardiovascular care. Our technology offers rapid, accurate data on flow dynamics and anatomy, aiding in the detection of microvascular disease. With an automated workflow, AngioInsight ensures precision and reliability in cardiovascular diagnostics. Job Description: We are seeking a highly motivated and passionate Machine Learning Engineer to join our team. This is an exciting opportunity for individuals looking to apply their knowledge of machine learning in the medical field. As a Machine Learning Engineer, you will work closely with the Lead Engineer and a multidisciplinary team to develop and deploy machine learning models and pipelines, particularly for medical image processing tasks. Key Responsibilities: Medical Image Processing : Assist in the design, development, and implementation of machine learning models specifically for medical image processing, under the guidance of the Lead Engineer. Data Preparation : Help prepare and preprocess large medical imaging datasets (e.g., X-ray coronary angiograms, CT coronary angiograms), including cleaning, transforming, managing labeling, and feature engineering. Model Training: Support the training and fine-tuning of machine learning models for tasks such as segmentation, classification, and detection in medical images. Model Evaluation : Help evaluate model performance using appropriate and assist in optimizing for accuracy and efficiency. Collaboration : Work with data scientists, software engineers, and cardiologists to integrate machine learning models into healthcare applications. Learning and Growth : Stay updated on the latest developments in medical image processing, machine learning, and AI applications in healthcare, and apply these advancements to projects. Reporting : Report directly to the Lead Engineer, who will provide mentorship and oversee your development. Required Skills and Qualifications: Education : Master’s degree in Computer Science, Electrical Engineering, Data Science, Biomedical Engineering, or a related field. Experience : 1-2 years of experience in machine learning or related fields, with a strong preference for industry experience in medical imaging. Internships, academic projects, and prior exposure to medical imaging are also highly regarded and will be considered. Technical Proficiency : Strong understanding of machine learning algorithms and techniques, particularly those relevant to medical image processing and analysis (e.g., segmentation, classification). Familiarity with machine learning libraries such as Scikit-learn, TensorFlow, PyTorch. Experience in programming with Python, with practical knowledge of libraries used for image processing, such as OpenCV. Good understanding of data preprocessing techniques, particularly for medical imaging datasets. Knowledge of common evaluation metrics in medical image analysis (e.g., Dice score, IoU). Analytical Mindset : Strong problem-solving skills and the ability to apply theoretical concepts to real-world medical applications. Collaboration : Proven ability to work as a team player in a cross-disciplinary team, including healthcare professionals, with a strong willingness to contribute to collective goals and ensure successful project outcomes.