Research Senior Scientist AI​/ML – Agentic Systems

Research Senior Scientist AI/ML – Agentic Systems

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. Our AI/ML organization is building a team to transform how medicines are discovered, aiming to apply AI and machine learning across the entire drug discovery process, from target identification through development.

This requires discernment in selecting models and methods, and the creativity to adapt when they don´t. We work with foundational models, generative approaches, and autonomous systems, pairing tools 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 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 an 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.

• Develop agentқ 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.

• 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 (Lang Chain, Auto Gen, CrewAI, or similar).
• Experience building RAG systems, including vector databases, embedding models, and retrieval yield pipelines.
• Understanding of drug discovery processes and scientific research workflows.
• Strong problem‑solostrar skills and ability to translate complex scientific processes into computational workflows.

• 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 (Pub Med, Uni Prot, ChEMBL) and APIs.
• Experience deploying AI systems in production environments.
• Track record of publications or presentations on LLM applications.

• Ability to…