Research Scientist, Molecular AI Full-time

Research Scientist

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, protein–ligand 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:

Boston, MA

U.S. Base Salary Range:

$116,000.00 – $182,270.00

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 job. 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.