About the role
Design, implement, and evaluate novel methodologies for scientific discovery through artificial intelligence, non-exhaustively including techniques around post-training, inference-time optimization, interpretability, and experimental design. Application-specific benchmarking and interpretation: Invent and apply techniques for developing a deep understanding of the capabilities of deep learning models as they relate to specific biological data domains and life sciences research questions of interest. System Optimization: Develop approaches for inference-time optimization of interaction patterns with deep learning models, e.g., context optimization, intelligent sampling, etc. In addition to these specific technical areas, candidates will be required to participate in robust, repeatable team-based technical research and be effective communicators. Doctorate in relevant field OR Master's Degree in relevant field AND 3+ years related research experience OR Bachelor's Degree in relevant field AND 4+ years related research experience OR equivalent experience. Experience creating and using generative AI or other ML techniques in the life sciences. Experience working with biological data (e.g., genomics, transcriptomics, proteomics, microscopy), applying both advanced methods and standard bioinformatics tools. Proven track record in bioinformatic algorithm development, benchmarking, interpretation, and application. Experience innovating software, systems, or workflows that leverage generative AI-based systems to solve real-world problems in the life sciences. This includes techniques like context engineering, prompt optimization, and optimization of test-time compute. Experience creating robust, repeatable technical research artifacts as part of an interdisciplinary team. Experience publishing academic papers as a lead author or essential contributor.
