Research Scientist, Molecular AI

About the role: Research Scientist (AI-enabled drug discovery) focused on structure-guided small-molecule design. How you will contribute: Develop and iterate deep learning models across the molecular modeling stack (structure prediction, protein–ligand co-folding, affinity, and/or generative design). Build benchmarking/evaluation pipelines linking offline metrics to real-world performance. Integrate validated models into production-ready drug discovery workflows. Apply computational and data-analysis methods to structural/sequence datasets to guide model development. Use generative AI and predictive ML to design and prioritize chemical matter for research projects. Communicate results via talks, technical write-ups, and peer-reviewed publications. Collaborate with ML engineers, structural biologists, and software engineers to prototype/scale solutions. Skill and qualifications (required): Ph.D. in Computational Biology/Biophysics/CS/Computational Chemistry (or related), with ML focus in molecular modeling. Hands-on deep learning development/training/validation; architectures (transformers, equivariant NNs, diffusion models). Experience with modern structure prediction/co-folding (e.g., AlphaFold2/3, RoseTTAFold, Chai-1, Boltz) or comparable systems. Python; PyTorch and/or JAX. Scientific rigor; strong written/verbal communication; teamwork in multidisciplinary environments. Preferred experience/skills: Postdoctoral/industry experience in structure prediction or structure-based drug design. Binding affinity prediction experience. Publications/preprints (NeurIPS/ICML/ICLR). Cloud deployment (GCP/AWS/Azure) and/or GPU/HPC; agentic coding tools (e.g., Claude Code, Codex). Benefits (high level): Medical/dental/vision 401(k) match disability and life insurance tuition reimbursement paid volunteer time off/holidays sick time (up to 80 hours/year) vacation (up to 120 hours for new hires) #J-18808-Ljbffr Scorpion Therapeutics