Associate Principal AI Scientist

AstraZeneca • Durham, NC

1d • Hybrid

About The Position

AstraZeneca is seeking an Associate Principal AI Scientist to drive innovation in agentic AI, multi-agent systems, and digital twins. This role involves exploring new methodologies, designing, implementing, and optimizing algorithms for autonomous decision-making, coordination, and policy learning among agents and digital twins using techniques like Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and multi-agent reinforcement learning (MARL). The scientist will evaluate agent performance in the context of decision making, collaboration, competition, and uncertainty. Collaboration with cross-functional teams is essential to ensure knowledge transfer to IT engineering teams for IT solution builds and deployment. The position requires keeping pace with industry advancements by reviewing academic papers and attending conferences, publishing findings in peer-reviewed journals, and representing the company at scientific forums. The role also involves communicating technical concepts and results to both technical and non-technical audiences. AstraZeneca emphasizes a hybrid work model (3 days in office, 2 remote) to foster bold thinking and collaboration, aiming to inspire life-changing medicines. The company embraces change and new solutions, leveraging technology to deliver medicines quickly, affordably, and sustainably. It promotes a diverse workforce united by curiosity and a digitally-enabled environment impacting all business parts, from robotic process automation to machine learning for quality batches, while contributing to society and the planet. AstraZeneca is committed to building an inclusive environment where equal employment opportunities are available to all applicants and employees, and provides accommodations for disabilities.

Requirements

• Min Bachelor´s degree 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.

Nice To Haves

• 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.

Responsibilities

• Drive innovation in agentic AI, multi-agent systems, and digital twins, exploring new methodologies and applications.
• Design, implement, and optimize algorithms for autonomous decision-making, coordination, and policy learning among agents and digital twins using techniques like Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), and multi-agent reinforcement learning (MARL).
• Evaluate agent performance in the context of decision making, collaboration, competition, uncertainty.
• Collaborate with cross-functional teams ensuring knowledge transfer to IT engineering teams for IT solution builds and deployment.
• Keep pace with industry advancements by reviewing academic papers and attending conferences.
• Publish findings in peer-reviewed journals and represent the company at scientific forums.
• Communicate technical concepts and results to technical and non-technical audiences.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal
• Number of Employees: 5,001-10,000 employees