Research Senior Associate, Computational Chemstry

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Job Description

Job Description 

At Takeda, we are a forward-looking, world-class R&D organization that unlocks innovation and delivers transformative therapies to patients. By focusing R&D efforts on three therapeutic areas and other targeted investments, we push the boundaries of what is possible to bring life-changing therapies to patients worldwide. 

Objective / Purpose:  

Join a cross-functional discovery group to build and deploy GPU-accelerated AI/ML workflows that push the frontier of structure-based design across targeted protein degradation (TPD) and small molecules. The candidate will combine 3D generative modeling with physics-aware scoring, high-throughput automation, and rigorous benchmarking to improve hit finding, chemotype diversity, and lead optimization.

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Accountabilities:  

• Modeling & GenAI
  • Train/finetune diffusion, transformer, and equivariant GNN models for pocket-aware ligand design, pose/affinity prediction, and sequence/structure tasks (co-folding, multimer/ternary modeling).
  • Develop GenAI-driven workflows for ternary complex prediction (target–E3–glue), binding pocket detection, and recruitment/degradation modeling.
• Pipelines & Scale
  • Build end-to-end, reproducible pipelines for MSA/co-folding feature generation, data QC, training, and evaluation with experiment tracking; accelerate on multi-GPU/HPC and cloud.
  • Automate large-scale ligand/mutation screening with geometry/covalency checks, pose-quality assessment, and multi-model consensus ranking.
• Physics + AI
  • Integrate docking/MD/MM/GBSA or related physics-based components with learned models to improve prioritization for peptides (including non-canonical AAs) and small molecules.
• Data & Benchmarking
  • Curate complex structural and proteomics datasets (including TPD recruitment/degradation data); design retrospective/prospective benchmarks and report robust metrics.
• Productization & Collaboration
  • Package models as services/containers; deliver intuitive analysis apps/dashboards (e.g., Streamlit) and visualizations (e.g., PyMOL integrations).
  • Partner with medicinal chemists, structural biologists, and data scientists; plan experimental validation and communicate decisions clearly.
• Impact & Visibility
  • Contribute to internal methods notes; author publications/posters for leading ML and scientific venues.

Education & Competencies (Technical and Behavioral):  

• MS in a scientific discipline (or equivalent) with 1+ years relevant experience, or BS with 3+ years relevant experience
• TPD domain exposure (e.g., CRBN glues, E3 recruitment) and ternary complex or PPI modeling.
• Co-folding/structure prediction (e.g., AlphaFold-Multimer/ESMFold/OpenFold) and pocket-guided design.
• Physics/ML hybrid methods; peptide modeling with non-canonical amino acids.
• Data systems (SQL/NoSQL), MLflow/Kubeflow/SageMaker; deployment on HPC/cloud.
• Publications, preprints, or conference presentations; awards or open-source contributions.

Takeda Compensation and Benefits Summary

We understand compensation is an important factor as you consider the next step in your career. We are committed to equitable pay for all employees, and we strive to be more transparent with our pay practices. 

For Location:

Boston, MA

U.S. Base Salary Range:

$86,500.00 - $135,960.00

EEO Statement

Takeda is proud in its commitment to creating a diverse workforce and providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, gender expression, parental status, national origin, age, disability, citizenship status, genetic information or characteristics, marital status, status as a Vietnam era veteran, special disabled veteran, or other protected veteran in accordance with applicable federal, state and local laws, and any other characteristic protected by law.

Locations

Boston, MA

Worker Type

Employee

Worker Sub-Type

Regular

Time Type

Full time

Job Exempt

Yes

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