Takeda · 1 day ago
Research Scientist, AI/ML Foundational Models
Takeda is a leading global biotechnology company, and they are seeking a Research Scientist to develop and deploy foundational AI models that will transform drug discovery. The role involves building large-scale models and integrating diverse data types to ensure scientifically grounded and impactful outcomes in drug discovery.
BiotechnologyHealth CareManufacturingMedicalPharmaceutical
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
Qualification
Required
PhD degree in a scientific discipline (or equivalent), 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
Preferred
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
Proficiency in Python and ML libraries (PyTorch, TensorFlow, scikit-learn); familiarity with Unix tools
Excellent collaboration and communication skills
Benefits
Medical, dental, vision insurance
401(k) plan and company match
Short-term and long-term disability coverage
Basic life insurance
Tuition reimbursement program
Paid volunteer time off
Company holidays
Well-being benefits
Up to 80 hours of sick time
Up to 120 hours of paid vacation
Company
Takeda
Takeda is a biopharmaceutical company that researches and develops pharmaceutical drugs.
H1B Sponsorship
Takeda has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (45)
2024 (39)
2023 (38)
2022 (34)
2021 (44)
2020 (18)
Funding
Current Stage
Public CompanyTotal Funding
$2.46B2025-06-27Post Ipo Debt· $2.4B
2016-09-01Grant· $19.8M
2016-05-08Grant· $38M
Leadership Team
Recent News
2026-01-14
Pharmaceutical Technology
2026-01-11
Company data provided by crunchbase