[SK biopharmaceuticals] AI Research Engineer

About the role

We are seeking an AI Research Engineer to build an AI-driven drug discovery system as part of our Translational Research platform (TR-AX), accelerating hit-to-lead and lead optimization. The role will combine LLM-based AI agents with drug discovery AI models to automate the path from data to optimized drug candidates. The successful candidate will develop multi-agent AI systems and AI models for drug discovery, including molecular property prediction, molecular generation and optimization, and multi-parameter optim. The role will work closely with domain experts to apply these systems and models to real-world drug discovery workflows.

What you'll do

• Research and develop LLM-based multi-agent AI systems for automating drug discovery
• Integrate diverse scientific tools and models for agents to use, and design and optimize agent workflows
• Develop models for molecular property prediction, molecular generation/optimization, protein-ligand interaction prediction, and related tasks
• Support SAR analysis, scaffold hopping, and multi-parameter optimization (MPO) balancing activity, properties, ADMET, and synthetic accessibility
• Integrate agents into the Design-Make-Test-Analyze (DMTA) cycle to propose the next synthesis candidates
• Deploy the 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

Requirements

• Education: Master's degree or higher in a related field; or a Bachelor's degree in a related field with 3+ years of relevant development experience
• Major: Computer Science, Computer Engineering, Electrical Engineering, Industrial Engineering, Chemical Engineering, Biotechnology, Chemistry, Biology, Physics, or a related field
• Experience: 8–15 years of relevant industry experience in a related field
• Hands-on experience developing drug discovery AI models (e.g., molecular property prediction, molecular generation/optimization, protein-ligand interaction prediction)
• Experience developing molecular optimization models or workflows at the hit-to-lead / lead optimization stage, including SAR-based design, MPO, or ADMET prediction
• Experience designing and building AI agent systems, including multi-agent systems and workflow orchestration
• Experience researching/developing deep learning and machine learning algorithms, with strong Python skills
• Research capability to read and implement recent papers, or to design and validate novel ML/DL methods
• Working proficiency in Korean and English

Preferred qualifications

• Publications at major AI conferences or journals (NeurIPS, ICML, ICLR, AAAI, etc.)
• Understanding of life-science domains (bio, pharma, chemistry, omics) or experience handling related data
• LLM experience in fine-tuning/post-training (SFT, DPO, PPO/GRPO/RLHF), RAG, serving/evaluation, and agent frameworks (LangGraph, LangChain, AutoGen, CrewAI)
• Experience participating in hit-to-lead / lead optimization on real drug discovery projects, or collaborating with medicinal chemistry teams to feed model results into design
• Experience developing, fine-tuning, or adapting domain foundation models for chemistry or biomedicine (e.g., molecular representation / chemical language models)
• Proficiency with ML frameworks (PyTorch, JAX, etc.) and experience designing/implementing novel ML/DL architectures and methods
• Experience implementing and deploying research and models as services or systems end-to-end

Benefits

자세한 사항은 홈페이지 참조

Hiring process

서류전형 → 필기전형(SKCT) → 면접전형 → 채용검진/처우협의 → 최종합격