Research Senior Scientist AI/ML – Agentic Systems

Takeda•Boston, MA

4d•$137,000 - $215,270

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 Senior Scientists to develop agentic AI systems that transform how drug discovery research is conducted. As part of the AI/ML Foundation team, you will build autonomous AI agents capable of reasoning, planning, and executing complex scientific workflows—from literature synthesis and target identification to experimental design and data analysis. This role requires a unique combination of expertise in large language models, agentic frameworks, and understanding of drug discovery processes. You will translate standard research workflows into agentic frameworks, develop new agent skills, and deploy systems that augment scientist productivity across Computational Sciences and Global Research.

Requirements

PhD in Computer Science, Computational Biology, Bioinformatics, or related field with 2+ years relevant experience, OR MS with 6+ years relevant experience.
Strong experience with large language models (GPT, Claude, Llama) and their application to complex reasoning tasks.
Proficiency in Python and experience with agentic AI frameworks (LangChain, AutoGen, CrewAI, or similar).
Experience building RAG systems including vector databases, embedding models, and retrieval pipelines.
Understanding of drug discovery processes and scientific research workflows.
Strong problem-solving skills and ability to translate complex scientific processes into computational workflows.

Nice To Haves

Experience in pharmaceutical or biotech R&D environments.
Background in biology, chemistry, or disease biology.
Experience with reinforcement learning or planning algorithms for agent decision-making.
Familiarity with scientific databases (PubMed, UniProt, ChEMBL) and APIs.
Experience deploying AI systems in production environments.
Track record of publications or presentations on LLM ap
Ability to lead cross-functional initiatives and mentor junior scientists.
Experience in translating computational insights into experimental strategies.
Strong publication record or demonstrated thought leadership in AI for biology and molecular design.
Comfort working in fast-paced, innovation-driven environments with evolving priorities.

Responsibilities

Develop agentic AI systems for drug discovery applications including target-disease association, automated literature search and synthesis, hypothesis generation, and intelligent design of experiments.
Translate standard research workflows into agentic frameworks—decomposing complex scientific processes into autonomous agent tasks that can reason, plan, execute tools, and iterate based on results.
Design and implement new agent skills (tools, functions, APIs) that extend agentic capabilities to specialized scientific domains including molecular design, property prediction, assay planning, and data analysis.
Build agentic systems that integrate with foundation models and external knowledge sources for autonomous hypothesis generation, evidence retrieval, and scientific reasoning.
Develop retrieval-augmented generation (RAG) pipelines connecting agents to internal and external scientific literature, databases, and experimental results.
Partner with research scientists to understand workflow needs, validate agent outputs, and iterate on system design to ensure scientific rigor and utility.
Stay current with advances in agentic AI, LLM applications, and scientific automation; contribute to internal knowledge sharing and external publications.

Benefits

U.S. based employees may be eligible to participate in medical, dental, vision insurance, a 401(k) plan and company match, short-term and long-term disability coverage, basic life insurance, a tuition reimbursement program, paid volunteer time off, company holidays, and well-being benefits, among others.
U.S. based employees are also eligible to receive, per calendar year, up to 80 hours of sick time, and new hires are eligible to accrue up to 120 hours of paid vacation.

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