Agentic AI Scientist, AstraZeneca
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Durham, North Carolina, USA We are looking for Agentic AI Scientists eager to utilize their expertise in these advanced technologies to revolutionize our drug development processes. In 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 into cross functional global pharmaceutical development projects in support of transforming the way 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 (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, R.
- Hands-on industrial experience designing multi-agent patterns, digital twins and experience with agentic AI design patterns, reinforcement learning.
- Extensive industrial experience with AI and ML frameworks like TensorFlow, PyTorch,
- Hands-on experience with GenAI orchestration frameworks such as LangGraph, 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, GenAI.
Desirable skills/experience:
- Contributions to open-source projects. If you meet these criteria, 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 such as transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI.
