As a Senior Machine Learning/Artificial Intelligence (ML/AI) Engineer on Ginkgo’s Biosecurity Team, you will leverage Ginkgo’s wealth of proprietary sequence and experimental data to design, train, and experimentally validate novel foundation and application-specific (e.g. fine-tuned) AI models for application to classification and design of genes, regulatory elements, multi-gene pathways, and even genomes. In addition, you will collaborate with experts throughout the organization to identify transformational opportunities for application of AI in genetic design and engineering, bioinformatics, and prediction/forecasting using a diverse array of multimodal data
Requirements:
PhD graduate with 1+ years of relevant post-academic experience in applying AI/ML (or BS 7+year OR MS 5+ years of professional experience); Hands-on, proven experience in developing deep neural networks. Deep knowledge of currently available AI model architectures and data schemes. Perspective on advantages and drawbacks of various approaches. Direct experience applying AI/ML to problems in modeling of RNA or DNA preferred; Subject matter expertise in genetics, genomics, transcription, translation, or RNA biology
Text:
Senior AI/ML Engineer As a Senior Machine Learning/Artificial Intelligence (ML/AI) Engineer on Ginkgo’s Biosecurity Team, you will leverage Ginkgo’s wealth of proprietary sequence and experimental data to design, train, and experimentally validate novel foundation and application-specific (e.g. fine-tuned) AI models for application to classification and design of genes, regulatory elements, multi-gene pathways, and even genomes. In addition, you will collaborate with experts throughout the organization to identify transformational opportunities for application of AI in genetic design and engineering, bioinformatics, and prediction/forecasting using a diverse array of multimodal data PhD graduate with 1+ years of relevant post-academic experience in applying AI/ML (or BS 7+year OR MS 5+ years of professional experience); Hands-on, proven experience in developing deep neural networks. Deep knowledge of currently available AI model architectures and data schemes. Perspective on advantages and drawbacks of various approaches. Direct experience applying AI/ML to problems in modeling of RNA or DNA preferred; Subject matter expertise in genetics, genomics, transcription, translation, or RNA biology
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