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Mid-level ML role in a metro location with broad skill requirements increases applicant competition.
Core ML, deep learning, and MLOps skills are broadly transferable across industries.
Explicit 5–8 years plus required ML, deep learning, deployment and MLOps skills make filters strict.
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Develop, optimize, and deploy machine learning and deep learning models to meet project objectives and operational standards.
Process and analyze large datasets to support model development, integration, and enhancements.
Collaborate with cross-functional teams to integrate machine learning solutions and maintain scalable software codebases.
5 to 8 years of work experience in machine learning engineering or related roles.
Proficiency in Python and machine learning/deep learning libraries such as numpy, pandas, scikit-learn, xgboost, pytorch, tensorflow.
Experience with deployment and maintenance of machine learning and deep learning models in production code.
Knowledge of software development tools including Git and virtual environments.
Experienced in applying a variety of machine learning methods including statistics, clustering, classification, regression, and outlier analysis.
Skilled in deep learning techniques such as Deep Neural Networks, Computer Vision, NLP, and Transformers, preferably with expertise in PyTorch.
Familiar with building and managing MLOps pipelines, API endpoints, and cloud platforms like AWS, ideally with exposure to Life Sciences-related models.