Research Scientist AI/ML Foundational Models

3316 Takeda Development Center Americas•Boston, MA

20h•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.

Requirements

PhD in Computer Science, Machine Learning, Computational Biology, Bioinformatics, or related field or MS with 6+ years relevant experience, or BS with 8+ years relevant experience
Deep expertise in modern deep learning architectures including transformers, diffusion models, and/or generative models.
Strong experience training large-scale models with proficiency in PyTorch and distributed training frameworks.
Foundational knowledge of biology, chemistry, or disease biology sufficient to guide scientifically meaningful model development.
Experience with at least one of: protein language models (ESM, ProtTrans), molecular generative models, or biomedical vision models.
Experience with cloud computing (AWS, GCP) and GPU cluster training at scale.
Proficiency in Python and ML libraries (PyTorch, TensorFlow, scikit-learn); familiarity with Unix tools.
Excellent collaboration and communication skills.

Nice To Haves

Experience building or fine-tuning foundation models in pharmaceutical or life sciences settings.
Expertise in multimodal learning integrating text, images, and structured molecular data.
Experience with omics data analysis (genomics, transcriptomics, proteomics) and knowledge graph
Familiarity with protein structure prediction and 3D molecular representations.
Publications in top-tier ML venues (NeurIPS, ICML, ICLR) or computational biology journals.
Experience with model compression, efficient inference, or production deployment of large models.
Strong background in large-scale data integration and multimodal modeling for biological systems.

Responsibilities

Develop and train foundational AI models (LLMs, diffusion models, flow-matching architectures) for drug discovery applications, with capability to pre-train on large-scale scientific corpora and molecular datasets.
Fine-tune and adapt pre-trained foundation models (protein language models, chemical LLMs, vision transformers) for Takeda-specific applications in target identification, disease modeling, and molecular design and discovery.
Build multimodal foundation models integrating diverse data types including omics (genomics, transcriptomics, proteomics), biomedical imaging, protein 3D structures, and molecular representations.
Apply and extend state-of-the-art approaches including graph neural networks, transformer-based protein language models, and multimodal learning frameworks.
Apply domain expertise in biology, chemistry, and/or disease biology to guide model architecture decisions, training data curation, and evaluation strategies ensuring scientific validity.
Implement state-of-the-art generative architectures (diffusion, score-based models, autoregressive transformers) for molecular generation, protein design, and multi-objective optimization.
Collaborate with computational scientists across domains to deploy foundation models that address diverse discovery needs across small molecules, biologics, and emerging modalities.
Stay current with advances in foundation models, generative AI, and multimodal learning; contribute to internal knowledge sharing and external publications.