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Moderate competition due to general ML title and unspecified experience, tempered by clinical dataset specialization.
Heavy clinical dataset and research pipeline focus makes transitions across industries difficult.
Specialized ML and clinical dataset experience, PyTorch/GPU and reproducibility requirements make filtering stringent.
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Design and execute machine learning experiments across architectures, hyperparameters, and random seeds with rigorous reproducibility.
Develop, implement, and debug ML model training pipelines including architectures, loss functions, augmentations, and training procedures.
Manage clinical dataset preprocessing and data pipelines supporting multi-site validation, generating outputs to support publications and patents.
Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, Data Science, or related field, or equivalent demonstrable experience.
Strong Python programming skills and hands-on experience with deep learning frameworks, preferably PyTorch.
Solid understanding of ML fundamentals including training, evaluation, overfitting, cross-validation, and metrics interpretation.
Work Experience Required: Not explicitly mentioned in the JD.
Operates effectively in a research-focused engineering role producing rigorous and reproducible experimental results.
Experienced handling complex real-world clinical datasets and supports multi-disciplinary collaborations.
Capable of managing multiple parallel experiments and maintaining detailed documentation for reproducibility and collaboration.