Machine Learning Engineer

About the Role: We are seeking a highly skilled and motivated Machine Learning Engineer to join the BRAID team (Biology Research | AI Development) within our Computational Sciences organization. Our team is dedicated to pioneering Client machine learning methods that transform drug development and clinical trial design. This includes multimodal generative models, representation learning, and reinforcement learning. We aim to develop machine learning models that offer both scientific innovation and tangible benefits to healthcare outcomes. As a key contributor to high-visibility projects, you will have the opportunity to publish in top-tier conferences and journals while performing science that will drive impact for clinical trial pipelines. We are looking for exceptional researchers and engineers with a strong foundation in machine learning fundamentals, a passion for interdisciplinary research, and a proven ability to transform research ideas into practical applications.
Responsibilities:
● Design and implement Client machine learning algorithms to understand associations between imaging and omics data.
● Collaborate with cross-functional teams, including machine learning scientists, imaging scientists and computational biologists, to integrate machine learning solutions into disease understanding and clinical decision-making. ● Analyze complex biological and clinical data to derive insights and guide decision-making in drug development and trial design. ● Stay informed about the latest developments in machine learning and their applications in healthcare and clinical trials. ● Publish findings in relevant journals and conferences. Qualifications:
● Educational Background: M.S. in Computer Science, Machine Learning, Statistics, Mathematics, Physics, Bioinformatics, Bioengineering, or a related quantitative field. ● Experience: Proven track record of developing and applying advanced ML models in research or industry settings. ● Technical Skills: ○ Proficiency in scientific programming languages such as Python and extensive experience with machine learning frameworks and libraries (e.g., JAX, PyTorch, TensorFlow). ○ Experience with MLOps workflows, including code version control, high-performance compute infrastructures, and machine learning experiment monitoring workflows. ○ Ability to build and deploy machine learning pipelines for scientific analysis. ● Soft Skills: Excellent communication, collaboration, and problem-solving skills. Preferred Qualifications: ● Experience working and analyzing multimodal data, such as: ○ Omics (genomics, transcriptomics, etc.), particularly with multivariate GWAS ○ Imaging and methods of image-based representation learning ● Familiarity with multimodal data integration and cross domain mapping strategies.