By clicking the “Apply” button, I understand that my employment application process with Takeda will commence and that the information I provide in my application will be processed in line with Takeda’s Privacy Notice and Terms of Use.  I further attest that all information I submit in my employment application is true to the best of my knowledge.

Job Description

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

We are seeking a Senior Scientist to lead the design and implementation of AI orchestration systems within the AI/ML Foundation team. This role combines expertise in agentic AI systems, data engineering, and software architecture to build the infrastructure that enables seamless coordination of AI capabilities across Computational Sciences and Global Research. You will be responsible for creating intelligent orchestration layers that connect diverse AI models, data pipelines, and computational resources to accelerate drug discovery across all therapeutic modalities. The ideal candidate brings strong engineering fundamentals with experience in distributed systems and a passion for building scalable AI platforms.

Accountabilities:

• Design and implement AI orchestration frameworks that integrate agentic systems, data pipelines, and model serving infrastructure to enable coordinated multi-model workflows.
• Build scalable orchestration layers connecting predictive models, generative models, and foundation models with experimental data sources for end-to-end workflow automation.
• Develop data engineering solutions including data ingestion pipelines, transformation workflows, feature stores, and model serving infrastructure supporting discovery across modalities.
• Create intelligent orchestration systems that coordinate agentic AI components for autonomous task decomposition, tool selection, and execution across scientific domains.
• Establish monitoring, observability, and governance frameworks ensuring reliability, reproducibility, and transparent decision-making across AI systems.
• Partner with computational scientists, data engineers, and research teams to ensure orchestration infrastructure meets the needs of diverse discovery workflows.
• Stay current with advances in agentic AI, workflow orchestration, and distributed systems; evaluate and integrate emerging technologies.

Education & Requirements:

• PhD in Computer Science, Data Engineering, Computational Science, or related field with 2+ years relevant experience, OR MS with 6+ years relevant experience.
• Strong software engineering skills with proficiency in Python and experience with distributed systems and cloud infrastructure (AWS, GCP).
• Experience with workflow orchestration frameworks (Airflow, Prefect, Dagster, or similar) and data pipeline development.
• Familiarity with agentic AI frameworks (LangChain, AutoGen, or similar) and LLM integration patterns.
• Experience with containerization (Docker, Kubernetes) and microservices architecture.
• Strong problem-solving skills and ability to work across teams in a fast-paced R&D environment.

Preferred:

• Experience building AI/ML platforms or infrastructure in pharmaceutical or life sciences settings.
• Familiarity with scientific computing workflows and computational chemistry/biology tools.
• Experience with model serving frameworks (TorchServe, Triton, BentoML) and feature stores.