Work Modality: Hybrid (3 days office and 2 remote)
Accountabilities:
- 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.
Essential skills/Experience:
- 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.
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.
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world.
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.
Ready to make a difference? Apply now to join our team!
Date Posted
20-abr-2026
Closing Date
19-may-2026
Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.