Nahin Khan is a machine learning engineer at Cradle with nine years of experience building ML-driven solutions at the intersection of computational biology and full-stack software engineering. Trained at Carnegie Mellon (dual BScs) and ETH Zürich (MSc in Computational Biology & Bioinformatics), he has hands-on experience deploying transformer and graph neural network models as well as end-to-end web and DevOps stacks (Django/FastAPI, React, Docker, Kubernetes, AWS/GCP). His work spans protein design, multimodal healthcare representation learning, and collaborative human-AI knowledge tools, blending research rigor with production-grade engineering. Past roles include bioinformatics research at QCRI and real-time collaborative tooling at IVIA Lab, and he’s shipped CI/CD and Kubernetes-backed products for startups. Unusually, his background couples wet-lab research experience (molecular biology and genomics) with deep software craftsmanship, enabling him to translate biological questions into scalable ML systems.
9 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Computational Biology and Bioinformatics, Master of Science - MS, Computational Biology and Bioinformatics at ETH Zürich
Bachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Carnegie Mellon University in Qatar
Contributions:79 pushes, 2 branches in 3 years 5 months
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