Research Scientist, Molecular AI

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Job Description

About the role:

We are seeking a Research Scientist to help shape the future of AI-enabled drug discovery at Takeda with a focus on structure-guided small-molecule design. Working across AI/ML structural biology and medicinal chemistry you will develop cutting-edge computational approaches to explore chemical space more effectively and translate scientific advances into life-saving therapeutic impact.

How you will contribute:

• Develop and iterate on deep learning models across the molecular modeling stack structure prediction proteinligand co-folding affinity and/or generative design building on the latest research from the field.
• Design and execute rigorous benchmarking and evaluation pipelines that connect offline metrics to real-world performance and hold models to a high scientific bar.
• Partner with senior scientists and engineers to integrate validated models into production-ready drug discovery workflows.
• Apply computational and data-analysis methods to structural and sequence datasets to generate insights that guide model development.
• Apply generative AI and predictive ML models to design and prioritize chemical matter for research projects.
• Communicate findings through internal scientific talks technical write-ups and contributions to peer-reviewed publications.
• Collaborate across multidisciplinary teams ML engineers structural biologists and software engineers to prototype and scale impactful solutions.

Skill and qualifications

• Ph.D. in Computational Biology Biophysics Computer Science Computational Chemistry or a related field with a research focus in ML for molecular modeling (e.g. structure prediction co-folding affinity or molecular design).
• Hands-on experience developing training and validating deep learning models including architectures relevant to structural biology and chemistry (e.g. transformers equivariant neural networks diffusion models).
• Direct experience with modern structure prediction or co-folding methods (e.g. AlphaFold2/3 RoseTTAFold Chai-1 Boltz) or comparable molecular ML systems.
• Strong proficiency in Python and modern ML frameworks (PyTorch and/or JAX).
• Demonstrated scientific rigor: the ability to design controlled experiments interpret results critically and iterate effectively on model development.
• Strong written and verbal communication skills and the ability to collaborate in a fast-paced multidisciplinary research environment.

Preferred experience:

• Postdoctoral or industry experience in structure prediction structure-based drug design or a related computational domain.
• Familiarity with binding affinity prediction including structure-based or physics-informed approaches.
• Authorship of publications or preprints in relevant venues (e.g. NeurIPS ICML ICLR).
• Experience deploying ML workflows on public cloud infrastructure (GCP AWS or Azure) and/or GPU/HPC environments.
• Familiarity with agentic coding tools (e.g. Claude Code Codex) to accelerate research prototyping.

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:

U.S. Base Salary Range:

The estimated salary range reflects an anticipated range for this position. The actual base salary offered may depend on a variety of factors including the qualifications of the individual applicant for the position years of relevant experience specific and unique skills level of education attained certifications or other professional licenses held and the location in which the applicant lives and/or from which they will be performing the actual base salary offered will be in accordance with state or local minimum wage requirements for the job location.

For information about our benefits please click here.

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.

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