Research Scientist, Molecular AI
Takeda
August 18, 2026
On-site
Boston, MA
$116,000 - $182,270 USD yearly
Clinical Research and Development
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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) and vacation (up to 120 hours for new hires).
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) and vacation (up to 120 hours for new hires).