2. Machine Learning Engineer
Role Summary:
MLE will play a critical role in developing and implementing machine learning solutions that drive innovation and efficiency across our projects. The ideal candidate will possess a strong foundation in machine learning, data processing, and software development, with the ability to work seamlessly with existing models and deploy new models into production.
Key Responsibilities:
• Develop, optimize, and deploy machine learning models to support project objectives.
• Process and analyze large datasets to inform model development and enhancements.
• Maintain and improve existing machine learning models within our systems, ensuring they meet our standards for accuracy and performance.
• Collaborate with cross-functional teams to integrate machine learning solutions into our operational framework.
• Utilize software development tools to maintain a robust, scalable codebase.
• Apply a variety of machine learning techniques (e.g., statistics, clustering, classification, outlier analysis) to solve complex problems.
Basic Qualifications:
• Proficiency in Python and knowledge of key machine learning packages such as numpy, pandas, scikit-learn, and xgboost.
• Understanding of data structures, data modeling, software architecture, and software development tools, including Git and virtual environments.
• Demonstrated ability to apply machine learning techniques to solve real-world problems.
• Experience in deploying machine learning models in code and working with existing models.
• Strong problem-solving skills and the ability to work independently or as part of a team.
Preferred qualifications:
• Experience in building and accessing API endpoints.
• Cloud computing experience, particularly with AWS.
• Experience with deploying open source deep learning models, with a preference for those related to Life Sciences.
• Proficiency in Deep Learning frameworks, such as PyTorch.
• Experience in building and managing MLOps pipelines.
Role Summary:
MLE will play a critical role in developing and implementing machine learning solutions that drive innovation and efficiency across our projects. The ideal candidate will possess a strong foundation in machine learning, data processing, and software development, with the ability to work seamlessly with existing models and deploy new models into production.
Key Responsibilities:
• Develop, optimize, and deploy machine learning models to support project objectives.
• Process and analyze large datasets to inform model development and enhancements.
• Maintain and improve existing machine learning models within our systems, ensuring they meet our standards for accuracy and performance.
• Collaborate with cross-functional teams to integrate machine learning solutions into our operational framework.
• Utilize software development tools to maintain a robust, scalable codebase.
• Apply a variety of machine learning techniques (e.g., statistics, clustering, classification, outlier analysis) to solve complex problems.
Basic Qualifications:
• Proficiency in Python and knowledge of key machine learning packages such as numpy, pandas, scikit-learn, and xgboost.
• Understanding of data structures, data modeling, software architecture, and software development tools, including Git and virtual environments.
• Demonstrated ability to apply machine learning techniques to solve real-world problems.
• Experience in deploying machine learning models in code and working with existing models.
• Strong problem-solving skills and the ability to work independently or as part of a team.
Preferred qualifications:
• Experience in building and accessing API endpoints.
• Cloud computing experience, particularly with AWS.
• Experience with deploying open source deep learning models, with a preference for those related to Life Sciences.
• Proficiency in Deep Learning frameworks, such as PyTorch.
• Experience in building and managing MLOps pipelines.