Agentic AI Scientist

Posted 5 Days Ago
Be an Early Applicant
Durham, NC, USA
In-Office
Mid level
Biotech • Pharmaceutical
The Role
Apply agentic AI, multi-agent systems, and digital twins to pharmaceutical development. Design and optimize RL/MARL algorithms (MDP/POMDP), evaluate agent performance, collaborate across teams to deploy IT solutions, publish research, and communicate results to varied audiences.
Summary Generated by Built In

Are you a driven Data Scientist with a robust foundation in traditional data science methods and a passion for Agentic AI, and human-in-the-loop (HITL) multi-agent systems? If so, an exciting opportunity awaits you at AstraZeneca!

We are looking for Associate Principal AI Data 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. PT&D 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.

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).

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:

  • 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.

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

09-jun-2026

Closing Date

06-jul-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.

Skills Required

  • PhD in computer science, data science, AI, machine learning, or related field
  • At least 3 years of experience in Deep Learning and Machine Learning
  • Excellent coding skills in Python and R
  • Industrial experience designing multi-agent patterns, digital twins, and agentic AI design patterns
  • Experience with reinforcement learning (MDPs, POMDPs) and multi-agent reinforcement learning (MARL)
  • Experience with ML frameworks such as TensorFlow and PyTorch
  • Experience with GenAI orchestration frameworks such as LangGraph and CrewAI
  • Experience with reinforcement learning libraries such as OpenAI Gym, Ray RLlib, or Stable Baselines
  • Industrial experience in applied ML domains (deep learning, NLP, GenAI)
  • Contributions to open-source projects (merged GitHub PRs)
  • Strong publication record in AI
  • Experience designing multi-agent systems in the pharmaceutical sector
  • Experience delivering ML projects in pharmaceutical development, chemical engineering, or chemistry
  • Experience with transfer learning, federated learning, few/zero-shot learning, meta-learning, or explainable AI

AstraZeneca Compensation & Benefits Highlights

The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about AstraZeneca and has not been reviewed or approved by AstraZeneca.

  • Fair & Transparent Compensation Pay is considered competitive across many roles when total rewards are factored in. Senior scientific and leadership bands are described with high ranges that reinforce competitiveness at upper levels.
  • Strong & Reliable Incentives Bonuses, equity eligibility in many salaried roles, and solid sales on‑target earnings with upside are emphasized as meaningful parts of compensation. These elements boost overall value even where base pay is not the very highest.
  • Retirement Support A 401(k) program with a strong company match and immediate vesting is repeatedly cited as a standout. Generous retirement support is viewed as enhancing the total package relative to peers.

AstraZeneca Insights

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The Company
HQ: Gaithersburg, MD
70,000 Employees
Year Founded: 1999

What We Do

We're transforming the future of healthcare by unlocking the power of what science can do for people, society and the planet.

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