Research Scientist, Molecular AI

About the role:

Research Scientist (AI-enabled drug discovery) focused on structure-guided small-molecule design.

• 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.

• 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.

• 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).

• 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)