We are seeking a talented Machine Learning Engineer who could contribute to our efforts in cross-modality transfer learning, particularly focusing on applications within healthcare. In this role, you will be instrumental in developing advanced machine learning algorithms and techniques to integrate data from diverse sources, thereby enhancing the efficiency and effectiveness of clinical decision-making processes. You will collaborate closely with world-class clinicians and cross-functional teams to understand requirements, design scalable solutions, and with the software development team to deliver high-quality, production-ready code.

Master’s (with 3-5 years of experience) or Ph.D. degree in Computer Science, Engineering, Statistics, Applied Mathematics, Biomedical Informatics, or a related field with a focus on machine learning, artificial intelligence or related field.

Strong programming skills in languages such as Python, C++ , and proficiency in relevant libraries and frameworks for machine learning and data analysis (e.g., TensorFlow, scikit-learn, PyTorch).

Excellent problem-solving skills and the ability to work independently as well as collaboratively in multidisciplinary teams.

Strong communication skills with the ability to effectively communicate technical concepts to non-technical stakeholders, including clinicians and healthcare professionals.

(optional but desirable) Understanding of clinical workflows, healthcare data standards (e.g., HL7, DICOM), and regulations (e.g., HIPAA), with experience working in healthcare or biomedical research environments preferred.

(optional but desirable) Experience with handling and processing large-scale multi-modal datasets, including EHR data, medical imaging data, genomics data, digital pathology and sensor data from wearable devices.