About the job
Core
Build an AI-driven drug discovery system combining LLM-based multi-agent AI systems with drug discovery AI models to automate the path from data to optimized drug candidates.
Role type
Senior AI Research Engineer (Drug Discovery)
Builds
Multi-agent AI systems and AI models for molecular property prediction, generation, optimization, and protein-ligand interaction prediction.
Domain
Biotechnology / Pharmaceutical R&D / Artificial Intelligence
Deliverable
production ML models
Required skills
Deep learning and machine learning algorithm development, Python, molecular property prediction, molecular generation/optimization, protein-ligand interaction prediction, multi-agent system design, workflow orchestration, DMTA cycle integration, system deployment and reliability improvement.
Preferred skills
Publications at major AI conferences, life-science domain understanding, LLM fine-tuning/post-training, RAG, agent frameworks, domain foundation models for chemistry/biomedicine, novel ML/DL architecture design, end-to-end service implementation.
Technologies
PyTorch, JAX, LangGraph, LangChain, AutoGen, CrewAI
Responsibilities
Research and develop LLM-based multi-agent AI systems for automating drug discovery; Integrate diverse scientific tools and models for agents to use; Develop models for molecular property prediction, molecular generation/optimization, and protein-ligand interaction prediction; Support SAR analysis, scaffold hopping, and multi-parameter optimization (MPO); Integrate agents into the Design-Make-Test-Analyze (DMTA) cycle to propose next synthesis candidates; Deploy systems and models into usable form and continuously improve performance and reliability; Collaborate with domain experts to apply research, models, and agent workflows to real drug discovery programs.
Seniority
Senior, hands-on IC
