Agentic AI Scientist

We are seeking Agentic AI Scientists eager to utilize their expertise in advanced technologies to revolutionize our drug development processes. Within the Pharmaceutical Technology and Development (PT&D) department, you will be a key player in transforming molecules into groundbreaking medical treatments. Your role involves contributing data science expertise to cross-functional global pharmaceutical development projects, aiming to transform how we deliver medicines to patients. You'll play a pivotal role in shaping our AI strategy and driving the co-development of sophisticated HITL multi-agent systems.

We are hiring two candidates for this position, and the roles 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 related fields.
• At least 3 years of experience in Deep Learning and ML.
• 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 like TensorFlow and PyTorch.
• Hands-on experience with GenAI orchestration frameworks such as LangGraph and CrewAI.
• Hands-on experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable Baselines.
• Hands-on industrial experience with applied machine learning domains such as deep learning, NLP, and GenAI.

Desirable Skills/Experience

• Contributions to open-source projects (please highlight merged GitHub PRs in your application).
• Strong publication record in the field of AI.
• Experience designing multi-agent systems in the pharmaceutical sector.
• Experience delivering machine learning projects with applications in 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.