Machine Learning Scientist

Role Overview: DeCentralCode, a technology company with global operations, is seeking an ML/AI Engineer to lead the machine learning pipeline for a medical imaging application. In this role, you will be responsible for dataset curation, model deployment, and ensuring robustness and reliability of machine learning models. You will work with cutting-edge technologies and collaborate with various stakeholders to deliver actionable insights and tangible business value. Key Responsibilities: - Own the end-to-end machine learning pipeline, including data ingestion, model evaluation, and deployment. - Build and maintain dataset pipelines with multi-annotator workflow support. - Implement data augmentation strategies to enhance model generalization. - Ensure proper train/validation/test splits for reliable model performance. - Develop CNN-based multi-output ordinal classification models using architectures like ResNet and EfficientNet. - Optimize models for mobile and edge deployment using ONNX and TensorRT. - Implement domain adaptation techniques for robustness across different devices, lighting conditions, and user environments. - Build image quality assessment modules for input data filtering or scoring. - Implement uncertainty quantification to measure prediction confidence. - Apply probability calibration techniques for reliable output probabilities. - Set up statistical evaluation frameworks with reproducible experiments, consistent validation pipelines, and reliable performance metrics. Qualifications Required: - Must Have: - 4+ years of hands-on experience in deep learning and computer vision. - Strong proficiency in PyTorch and deep learning workflows. - Solid experience with CNN architectures such as ResNet, EfficientNet, or equivalent. - Good understanding of ordinal classification and multi-output learning techniques. - Expertise in data pipeline development, including preprocessing, augmentation, and validation. - Strong knowledge of statistical analysis and model evaluation methodologies. - Proficiency in Python and its data science ecosystem: NumPy, pandas, scikit-learn, matplotlib/seaborn. - Experience with Git version control and clean, maintainable coding practices. - Good to Have: - Experience working with medical imaging or healthcare AI solutions. - Knowledge of mobile and edge model optimization techniques (ONNX, TensorRT, CoreML). - Familiarity with domain adaptation and transfer learning methods. - Exposure to Bayesian deep learning and uncertainty quantification approaches. - Experience using data annotation tools and managing annotation workflows. Role Overview: DeCentralCode, a technology company with global operations, is seeking an ML/AI Engineer to lead the machine learning pipeline for a medical imaging application. In this role, you will be responsible for dataset curation, model deployment, and ensuring robustness and reliability of machine learning models. You will work with cutting-edge technologies and collaborate with various stakeholders to deliver actionable insights and tangible business value. Key Responsibilities: - Own the end-to-end machine learning pipeline, including data ingestion, model evaluation, and deployment. - Build and maintain dataset pipelines with multi-annotator workflow support. - Implement data augmentation strategies to enhance model generalization. - Ensure proper train/validation/test splits for reliable model performance. - Develop CNN-based multi-output ordinal classification models using architectures like ResNet and EfficientNet. - Optimize models for mobile and edge deployment using ONNX and TensorRT. - Implement domain adaptation techniques for robustness across different devices, lighting conditions, and user environments. - Build image quality assessment modules for input data filtering or scoring. - Implement uncertainty quantification to measure prediction confidence. - Apply probability calibration techniques for reliable output probabilities. - Set up statistical evaluation frameworks with reproducible experiments, consistent validation pipelines, and reliable performance metrics. Qualifications Required: - Must Have: - 4+ years of hands-on experience in deep learning and computer vision. - Strong proficiency in PyTorch and deep learning workflows. - Solid experience with CNN architectures such as ResNet, EfficientNet, or equivalent. - Good understanding of ordinal classification and multi-output learning techniques. - Expertise in data pipeline development, including preprocessing, augmentation, and validation. - Strong knowledge of statistical analysis and model evaluation methodologies. - Proficiency in Python and its data science ecosystem: NumPy, pandas, scikit-learn, matplotlib/seaborn. - Experience with Git version control and clean, maintainable coding practices. - Good to Have: - Experience working with medical imaging or healthcare AI solutions. - Knowledge of mobile and edge mo