Agentic AI Scientist - Pharmaceutical Technology and Development

About the Role

We are seeking highly motivated Agentic AI Scientists to leverage advanced technologies and revolutionize our drug development processes within the Pharmaceutical Technology and Development (PT&D) department. You will contribute essential data science expertise to cross-functional global pharmaceutical development projects, aiming to transform how medicines are delivered to patients. This pivotal role involves shaping our AI strategy and driving the co-development of sophisticated Human-in-the-Loop (HITL) multi-agent systems.

We are hiring two candidates for this position, which will be based at our dynamic site in Durham, North Carolina, USA.

Essential Skills & Experience

• PhD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related field.
• Minimum of 3 years of experience in Deep Learning and Machine Learning.
• Excellent coding skills in languages such as Python and R.
• Hands-on industrial experience designing multi-agent patterns, digital twins, and agentic AI design patterns, including reinforcement learning.
• Extensive industrial experience with AI and ML frameworks such as TensorFlow and PyTorch.
• Hands-on experience with Generative AI (GenAI) orchestration frameworks like LangGraph and CrewAI.
• Hands-on experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable Baselines.
• Hands-on industrial experience in applied machine learning domains, including deep learning, Natural Language Processing (NLP), and GenAI.

Desirable Skills & Experience

• Contributions to open-source projects (please highlight merged GitHub PRs in your application).
• A strong publication record in the field of AI.
• Experience designing multi-agent systems within the pharmaceutical sector.
• Experience delivering machine learning projects applied to pharmaceutical development, chemical engineering, or chemistry.
• Experience with one or more of the following applied machine learning domains: transfer learning, federated learning, few/zero-shot learning, meta-learning, explainable AI (XAI).