Role Description This part-time remote Machine Learning Engineer role focuses on designing, implementing, and optimizing machine learning models to support research and operational needs in construction and public works. Day-to-day tasks include collecting and preprocessing data from various engineering and infrastructure sources, experimenting with pattern recognition and neural network architectures, and evaluating models using appropriate statistical metrics. The role involves developing efficient algorithms, writing clean and maintainable code, and integrating models into existing tools or workflows in collaboration with engineers and researchers. The Machine Learning Engineer will document methods and results clearly, present findings to technical and non-technical stakeholders, and contribute to continuous improvement of data and modeling practices.
Qualifications
Candidates should possess strong Computer Science fundamentals and experience with Algorithms relevant to machine learning and data processing.
Candidates should possess skills in Pattern Recognition and Neural Networks for building and refining predictive and classification models.
Candidates should possess a solid background in Statistics for model evaluation, hypothesis testing, and experimental design.
Candidates should possess proficiency in one or more programming languages commonly used in machine learning (e.g., Python) and frameworks such as TensorFlow or PyTorch.
Candidates should possess the ability to work independently in a remote setting, manage part-time hours effectively, and communicate clearly with multidisciplinary teams.
Relevant academic background (e.g., degree in Computer Science, Data Science, Applied Mathematics, or related field) and experience with engineering or infrastructure datasets are beneficial.

Machine Learning Engineer

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