Machine Learning Engineer Intern contributing to AI solutions for financial services. Engaging in hands - on ML projects and real production issues in a hybrid working environment.
About the role
- AI/ML Engineer focusing on machine learning applications in oncology. Developing analytical solutions to enhance patient selection and drive next-generation assets in healthcare.
Responsibilities
- Design and implement novel scientific approaches for biophysical modeling and foundation model-driven analysis of multi-modal clinical and genomic data for biomarker and target discovery to improve patient selection and enable next-generation assets.
- Design, develop, and implement analytical solutions using a variety of commercial and open-source tools (common tools include PyTorch and scikit-learn).
- Connect and collaborate with subject matter experts in biology, genomics, and medicine.
- Identify opportunities to apply the latest advancements in Machine Learning and Artificial Intelligence to build, test, and validate predictive models.
- Develop and embed automated and agentic processes for predictive model validation, deployment, and implementation.
- Deploy your algorithms to production to identify actionable insights from large databases.
Requirements
- Master’s degree in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field
- Experience in Python programming and knowledge in machine learning, statistics, and applied math.
- Familiarity with modern machine learning methods (generative models, representation learning)
- Experience in building deep learning models, preferably with exposure to biophysical modeling, functional genomics, molecular and cellular biology or to modeling dynamical systems
- Experience with at least one Deep Learning framework such as PyTorch
Benefits
- health care and other insurance benefits (for employee and family)
- retirement benefits
- paid holidays
- vacation
- paid caregiver/parental and medical leave
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