Agentic AI Scientist
Core
Designing and optimizing agentic AI, multi-agent systems, and digital twins to revolutionize drug development processes and synthetic routes.
Role type
Associate Principal AI Data Scientist (Agentic AI & Multi-Agent Systems)
Builds
Sophisticated HITL multi-agent systems and digital twins for pharmaceutical development
Domain
Pharmaceutical Technology and Development (PT&D) / Drug Discovery
Deliverable
production ML models
Required skills
Deep Learning, Multi-Agent Reinforcement Learning (MARL), Markov Decision Processes (MDPs), Partially Observable MDPs (POMDPs), GenAI orchestration, Reinforcement Learning libraries, Python, R, TensorFlow, PyTorch
Preferred skills
Open-source contributions, strong publication record, experience in pharmaceutical sector, transfer learning, federated learning, few/zero shot learning, meta learning, explainable AI
Technologies
LangGraph, CrewAI, OpenAI Gym, Ray RLlib, Stable Baselines
Responsibilities
Drive innovation in agentic AI and digital twins; Design and optimize algorithms for autonomous decision-making and policy learning; Evaluate agent performance in decision-making and collaboration contexts; Collaborate with cross-functional teams for IT solution deployment; Review academic papers and publish findings in peer-reviewed journals.
Seniority
Associate Principal, hands-on IC with strategy influence
