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Job Description Job Responsibilities AI/ML-Driven Design: Lead the team in developing or utilizing state-of-the-art machine learning tools for de novo protein or antibody design; spearhead the development of novel antibody discovery methodologies. Computational Tool Development: Develop or leverage open-source models, train them using internal datasets, fine-tune models as needed, and perform multi-parameter optimization of antibodies (e.g., affinity, selectivity, half-life, drug-like properties). Cross-Functional Collaboration: Work closely with antibody engineering, structural biology, and therapeutic teams to translate AI predictions into physical molecules and effectively conduct wet lab validation. Technology Exploration: Track breakthroughs in AI/ML and computational biology; lead external collaborations (e.g., with academia/AI companies) to build Innovent’s AI development ecosystem. Strategic Leadership: Develop the team’s AI/ML technology roadmap, cultivate computational biology talent, and drive innovative molecular entities into the pipeline. Qualifications 1.Education & Experience: Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a related field, with a focus on AI/ML applications in life sciences; minimum 3 years of relevant experience. Experience in de novo protein design and antibody engineering is . Prior success in leading AI/ML projects within pharmaceutical, biotech, or research institutions is highly desirable. Experience managing and developing ML/AI scientist teams; excellent communication skills to convey complex technical concepts to cross-functional colleagues; proven ability to collaborate effectively in interdisciplinary teams. 2.Technical Expertise: Deep knowledge of protein structure analysis, antibody biology, and therapeutic antibody design principles. Proficiency in protein modeling and structural analysis software (e.g., Rosetta, PyMOL, MOE, or equivalents). Extensive experience with generative AI; hands-on practice with cutting-edge ML tools (e.g., AlphaFold, RFdiffusion, ProteinMPNN, or similar models). Core competency in developing and implementing ML algorithms; proficiency in Python/R/C++ and deep learning architectures (e.g., CNNs, RNNs, Transformers). Expertise in advanced ML models for protein modeling, including generative models (diffusion, flow matching), models (Transformers), multimodal learning, geometric deep learning, and graph representation learning. 3.Results-Driven: Published original research on AI applications in biomedicine in top-tier journals/conferences. Delivered presentations on AI/ML topics at conferences on behalf of companies or research institutions. #J-18808-Ljbffr ZipRecruiter

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