Agentic AI Scientist

Python Software Foundation; Durham (NC); On-site; Confidential; Full time

Job summary

The Python Software Foundation is seeking Agentic AI Scientists in Durham, USA, to leverage advanced technologies for drug development. You will play a critical role in transforming molecules into groundbreaking treatments and contribute to innovative global projects in pharmaceuticals.

Ideal candidates will have a PhD and at least 3 years of deep learning experience. Join a dynamic environment focused on leveraging AI and machine learning for impactful solutions in healthcare.

Qualifications

• PhD in computer science, data science, AI, or related fields.
• At least 3 years of experience in Deep Learning and ML.
• Hands-on experience with AI and ML frameworks.

Responsibilities

• Contribute data science expertise to global pharmaceutical development projects.
• Shape AI strategy and drive co-development of multi-agent systems.
• Support transformation of molecules into medical treatments.

Skills

Deep Learning

Machine Learning

Python

Reinforcement Learning

Data Science

Education

PhD in computer science, data science, AI, or related fields

Tools

TensorFlow

PyTorch

OpenAI Gym

Job description

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).

Restrictions

• No telecommuting
• No Agencies Please

Requirements

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.
• 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.

About the Company

PT&D, AstraZeneca leads the charge in developing cutting‑edge synthetic routes, drug formulations and delivery technologies, ensuring our products are effective, safe, and of the highest quality.

AstraZeneca is a place where change is embraced, and new solutions are trialed with patients and business in mind. Here, technology is a key lever for delivering medicines quickly, affordably, and sustainably. Our diverse workforce is united by curiosity, sharing learnings to scale fast. Be part of a digitally‑enabled environment that impacts all parts of the business—from robotic process automation to machine learning for quality batches—while contributing to society and the planet.