AI Engineer (Life Sciences)

This mid-level AI Engineer role in the life sciences sector requires a solid foundation in artificial intelligence and machine learning principles, supported by a bachelor's degree. Candidates should have practical experience applying AI techniques to solve complex problems within life sciences, such as drug discovery, genomics, bioinformatics, or healthcare analytics. Key technical competencies likely include proficiency in programming languages commonly used in AI development, such as Python or R, along with experience using machine learning frameworks like TensorFlow, PyTorch, or scikit-learn. Familiarity with data preprocessing, model training, validation, and deployment in scientific contexts is essential. The role demands an understanding of biological data types and domain-specific challenges, enabling effective collaboration with interdisciplinary teams. Experience level suggests the candidate should be capable of independently developing AI models and contributing to project design, while also engaging in continuous learning to stay current with evolving AI methodologies and life sciences advancements. Strong analytical skills, problem-solving abilities, and effective communication are important to translate complex AI concepts into practical life sciences applications. Overall, the position seeks a candidate who combines technical AI expertise with a meaningful understanding of life sciences to drive innovation and deliver impactful solutions.