Research Senior Scientist, AI/ML (Biologics)

Takeda•Boston, MA

About The Position

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. The AI/ML organization at Takeda is building a team to transform how medicines are discovered. Our goal is to apply AI and machine learning across the entire drug discovery process, not just isolated steps, but as an integrated approach from target identification through development. This requires discernment: knowing which models and methods fit each problem, and the creativity to adapt when they don't. We work with foundational models, generative approaches, and autonomous systems, but the tools only matter when paired with people who understand the science deeply enough to use them well. Our team brings together computational scientists, biologists, engineers, and drug hunters. If you want to contribute your expertise to hard problems alongside colleagues with different perspectives, and help shape how AI delivers real impact in drug discovery, we'd like to hear from you. Position Overview We are seeking an innovative and dynamic AI/ML Research Senior Scientist with a passion for leveraging AI/ML in antibody discovery and design to join our Large Molecule AI/ML team. This role will be part of a multidisciplinary team focused on integrating advanced computational methods with cutting-edge experimental strategies to drive breakthrough discoveries in large molecule therapeutics and deepen our understanding of disease biology. The ideal candidate will have a strong background in computational biology, machine learning, and structural modeling and specifically with the application of AI/ML in biologics discovery.

Requirements

PhD degree in a scientific discipline (or equivalent) with 2+ years relevant experience, or MS with 8+ years relevant experience, or BS with 10+ years relevant experience
Proven track record in developing machine learning models for chemical and biological data, including AI/ML-enabled molecular generation and affinity prediction.
Demonstrated experience in modeling antibody/ antigen sequence, structure and interaction.
Proficiency in programming languages such as Python and experience with cloud computing capabilities.
Strong analytical and problem-solving skills, with demonstrated creativity and the ability to contribute individually and collaboratively.
Versatile communicator who can elucidate complex ideas to non-specialists and commitment to continuous improvement and innovation.
Demonstrated learning agility, and scientific curiosity while maintaining focus on driving greater impact in the face of uncertainty and change.
Strong problem-solving aptitude and strategic thinking with an entrepreneurial mindset.

Nice To Haves

Experience developing or applying modern ML architectures for antibody design models (LLMs, diffusion models, flow-matching, Bayesian Optimization, GNNs, etc.)
Experience designing de novo binders for specified targets and epitopes
Experience analyzing NGS-derived antibody repertoires for sequence- and structure-based design
Experience with molecular simulation and conformational analysis techniques

Responsibilities

Develop and implement state-of-the-art AI/ML methodologies for de novo antibody design and discovery, including fine-tuning protein language models and generative protein design.
Develop, implement, and deploy advanced machine learning algorithms for the multi-objective optimization of antibodies, antigens, ADCs, and other biologics.
Build tools to incorporate data from integrated Design-Predict-Make-Confirm cycles with automated experimental platforms generating quality data at scale needed for project-specific and foundational models.
Innovate, develop, and apply predictive models for protein design and developability engineering, utilizing large-scale NGS, in vitro, in vivo and other proprietary in-house and external data sources.
Manage and process large-scale biological datasets for model training and evaluation
Stay abreast of advancements in NLP, ML, and generative AI to build novel tools that enhance therapeutic discovery and development.
Collaborate with internal experts to optimize the computational discovery infrastructure, offering both individual and team-based innovative solutions.
Communicate complex scientific ideas effectively to both technical and non-technical audiences, fostering collaboration across multidisciplinary teams.

Benefits

U.S. based employees may be eligible to participate in medical, dental, vision insurance, a 401(k) plan and company match, short-term and long-term disability coverage, basic life insurance, a tuition reimbursement program, paid volunteer time off, company holidays, and well-being benefits, among others.
U.S. based employees are also eligible to receive, per calendar year, up to 80 hours of sick time, and new hires are eligible to accrue up to 120 hours of paid vacation.

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