Sr. Scientist, AI/ML

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Job Description

Position Overview

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 our core therapeutic areas and pioneering AI-driven platforms, we aim to accelerate the next generation of biologics discovery.

We are seeking an innovative and dynamic Senior Scientist with a strong background in computational biology, structural bioinformatics, and machine learning, particularly as applied to antibody and large molecule design. You will join our Large Molecule AI/ML team and contribute to a multidisciplinary group that integrates state-of-the-art AI with experimental strategies to drive therapeutic breakthroughs in oncology, neuroscience, and inflammatory diseases.

This execution-focused role will involve applying AI/ML and structural modeling techniques to design, optimize, and validate biologics, including antibodies, enzymes, and antibody-drug conjugates (ADCs).

Key Responsibilities

• Develop and implement AI/ML models for sequence- and structure-based design of biologics, with emphasis on generative frameworks (e.g., diffusion models, inverse folding, LMs).
• Apply and extend tools such as Boltz, Rosetta, AlphaFold2, ESMFold, ProteinMPNN, and other foundational models for de novo antibody discovery, affinity maturation, and developability optimization.
• Build and optimize predictive models for multiple objectives (e.g., solubility, immunogenicity, thermostability, epitope specificity) based on NGS, in vitro, and in vivo datasets.
• Integrate 3D structural data into model-guided protein design pipelines.
• Collaborate with experimental scientists to inform hypothesis generation, model validation, and iterative learning in Design–Make–Test–Analyze cycles.
• Manage and process large-scale experimental and synthetic datasets for model training, benchmarking, and deployment.
• Prototype and deploy ML pipelines using best practices in software engineering and reproducibility
• Stay current with the latest developments in NLP, structural modeling, and AI for protein science; evaluate emerging tools for integration.
• Clearly communicate complex ideas to technical and non-technical audiences and contribute to internal knowledge sharing.

Required Qualifications

• PhD degree in Computational Biology, Structural Biology, Machine Learning, or related fields (or equivalent) with 2+ years relevant experience, or MS with 8+ years relevant experience, or BS with 10+ years relevant experience
• Demonstrated ability to build and apply ML models (deep learning, protein LMs, GNNs) to biological or structural datasets.
• Hands-on experience with antibody design or protein engineering, especially using ML-guided or structure-aware methods.
• Strong understanding of protein structure and dynamics, including MD simulation, FEP, Rosetta modeling, or AlphaFold-based tools.
• Proficiency in Python and ML libraries (e.g., PyTorch, scikit-learn, NumPy); familiarity with Unix command line tools and scripting.
• Strong data wrangling and visualization skills; ability to translate modeling outputs into actionable insights for bench scientists.
• Excellent interpersonal and written communication skills; thrives in a highly collaborative environment.

Preferred Qualifications

• Experience developing or fine-tuning generative models for protein design (e.g., RFdiffusion, ProGen, ESM-IF, ProteinMPNN).
• Prior appli