Sr. Scientist, AI/ML Biologics

Takeda•Boston, MA

1d•Onsite

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 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 an embedded member of multidisciplinary project teams, integrating advanced computational methods with cutting-edge experimental strategies to advance biologics programs from discovery through candidate nomination and toward clinical development. The ideal candidate will have a strong background in computational biology, machine learning, and structural modeling, with demonstrated application of AI/ML in biologics discovery and optimization.

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 in fast-paced project team environments.
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.
Experience analyzing NGS-derived antibody repertoires for sequence- and structure-based design

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 with molecular simulation and conformational analysis techniques

Responsibilities

Develop and implement state-of-the-art AI/ML methodologies for antibody design and discovery.
Develop, implement, and deploy advanced machine learning algorithms for the multi-parameter optimization of biologics with a focus on delivering high-quality development candidates (antibodies, antigens, ADCs, and other modalities)
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.
Actively contribute as a core member of cross-functional project teams, partnering closely with biology, protein engineering, and data generation teams to drive programs toward candidate selection and IND-enabling stages.
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 and influencing project decisions.