Senior Engineer

SK바이오팜은 자체 개발한 뇌전증 혁신 신약 세노바메이트(미국 제품명: XCOPRI®)를 미국에 직접 출시하며 국내 제약사 최초로 글로벌 상업화에 성공했습니다. 미국 법인 SK Life Science를 중심으로 구축한 직판 체계와 글로벌 파트너십을 통해 북미를 비롯한 주요 시장에서 지속적으로 입지를 확대하고 있습니다.

이러한 성장 기반 위에서 방사성의약품(RPT), 표적단백질분해(TPD) 등 신규 Modality를 중심으로 차세대 Pipeline을 강화하고, 글로벌 오픈 이노베이션을 통해 연구개발 역량을 확장하고 있습니다. 또한 AI와 디지털 기술을 접목해 신약 개발부터 환자 치료 경험까지 연결하는 통합 헬스케어 생태계 구축을 추진하고 있습니다.

SK바이오팜은 ‘균형 잡힌 빅 바이오텍’ 비전을 바탕으로 지속적인 혁신과 성장을 통해 글로벌 헬스케어 산업 발전에 기여하고 있습니다

채용 포지션 : Senior Data Engineer, Senior LLM Engineer (2개 포지션에서 각각 채용 합니다.)

1. Senior Biomedical Data Engineer (AIDD, TR-AX - Biomedical Data Foundation)

Role Summary

We are seeking a Senior Biomedical Data Engineer to build the biomedical data foundation powering AI-driven drug discovery and translational research. As a pharmaceutical company developing AI-native R&D capabilities, we aim to integrate multimodal biomedical data into standardized, AI-ready datasets supporting foundation models, AI agents, and Digital Twin technologies.

The successful candidate will establish scalable biomedical data pipelines, ontologies, and knowledge engineering workflows that become core capabilities of our AI platform.

Key Responsibilities

• Design, develop, and maintain biomedical data pipelines integrating internal and external datasets.
• Develop standardized, AI-ready multimodal biomedical datasets for AI and foundation model development.
• Design and implement biomedical data models for structured and unstructured data.
• Develop and optimize ETL workflows to support large-scale biomedical data processing.
• Design biomedical ontologies, metadata standards, and knowledge graphs.
• Perform data harmonization, normalization, metadata management, and data quality validation across heterogeneous biomedical data sources.
• Collaborate with AI scientists to prepare AI-ready datasets supporting machine learning and biomedical foundation model development.
• Evaluate external biomedical databases and support long-term data acquisition strategies.
• Contribute to the continuous improvement of the biomedical data platform, data governance practices, and engineering practices.

Required Qualifications

• Bachelor's degree or higher in Bioinformatics, Computational Biology, Data Science, Data Engineering, Computer Science, or related fields.
• 8~15 Years of Experiences, including 6+ years of Experience designing and implementing biomedical or healthcare data platforms or large-scale data engineering solutions.
• Strong proficiency in Python and SQL.
• Hands-on Experience developing production-grade ETL pipelines and data integration workflows.
• Experience with cloud-based data platforms (AWS)
• Experience working with structure and unstructured multimodal biomedical or healthcare data.
• Strong understanding of data modeling, metadata management, and data quality assurance.
• Excellent communication and collaboration skills in cross-functional research environments.

Preferred Qualifications

• Experience with genomics, transcriptomics, proteomics, imaging, or other multimodal biomedical datasets.
• Experience with biomedical ontologies or knowledge graphs.
• Experience with LLM-based information extraction.
• Experience supporting foundation model development.
• Experience in AI-driven drug discovery.

2. Senior LLM Engineer (AIDD, TR-AX - Biomedical / Life Sciences)

Role Summary

We are seeking a hands-on LLM Engineer to help develop a specialized large language model for the biomedical domain. As a pharmaceutical company advancing drug discovery and life sciences research, we aim to build LLM capabilities that directly support scientific discovery — from understanding biomedical literature and biological data to accelerating the drug discovery pipeline.

In this role, the successful candidate will drive large-scale model training, ensure its stability and efficiency, and establish a reproducible, maintainable training process. The position offers the opportunity to build specialized LLM development into a durable in-house capability at the intersection of AI and life sciences.

Key Responsibilities

• Drive and monitor large-scale LLM training in a multi-GPU environment for a specialized biomedical model
• Take ownership of the training codebase and pipeline — understanding it in depth, operating it, and extending it as the project evolves
• Collaborate with external LLM vendors and technology partners to align on training approaches, tools, and workflows
• Diagnose and resolve training failures, instability, and performance bottlenecks
• Improve training throughput, memory efficiency, and overall job stability
• Support model evaluation throughout training, including domain-specific benchmarks relevant to drug discovery and life sciences
• Maintain well-organized experiments, checkpoints, and technical documentation to ensure reproducibility
• Collaborate with internal scientific and data teams to prepare training data, configurations, and workflows
• Establish a reliable, repeatable workflow for future model updates and scaling

Required Qualifications

• Bachelor's degree or higher in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative field.
• 8~15 Years of Experiences, including 6+ years of relevant industry experience in machine learning, large language models, or AI systems.
• Hands-on experience training or fine-tuning large language models
• Strong proficiency in Python and PyTorch
• Experience with distributed and multi-GPU training
• Ability to debug effectively using logs, metrics, and experiment outputs
• Solid understanding of training stability and performance optimization

Preferred Qualifications

• Experience applying AI or machine learning in biotech, pharmaceutical, or life sciences settings
• Familiarity with biomedical data, scientific literature, or domain-specific language models
• Experience with GPU cluster environments or Slurm
• Experience with LLM serving, inference optimization, or quantization
• Experience with evaluation frameworks or benchmark testing
• Experience collaborating with external vendors or technology partners

이러한 여정으로 진행됩니다.

• 서류접수 : ~’26. 09. 06
• 서류 접수 - 필기 전형(SKCT) - 면접 전형 - 채용 검진/처우 협의 - 최종 입사
• 전형 절차/일정은 상황에 따라 변동 될 수 있으며, 전형 결과에 따라 추가 절차(인터뷰 등)가 진행될 수 있습니다.
• 채용 과정 중 필요 시, 지원자의 경력 및 평판 확인을 위해 레퍼런스 체크가 진행될 수 있습니다.
• 이력서 제출 시 파일명 마지막에 포지션 명을 기재해주시기 바랍니다. (예: 홍길동_이력서_Senior LLM Engineer)

기타

• 국가보훈 대상자 및 장애인은관련법에 의거 우대합니다.
• 석,박사 학위 소지자의 경우 학사를 포함한 전체 학력 정보를 제출해주시기바랍니다.
• 써치펌과 SK Careers 포털, LinkedIn 등 채널 간 중복 지원은 불가능 합니다.
• 당사 채용 공고 간, SK그룹 계열사 간 중복 지원은 불가능 합니다.
• 각 전형 일정은 상황에 따라 조정될 수 있습니다.
• 병역필 또는 면제자로서 해외 여행에 결격 사유가 없는 분에 한하여 지원이 가능합니다.