Bioinformatics Job At GenBio AI in the UAE
The intersection of artificial intelligence and life sciences is transforming modern research, and Bioinformatics Jobs are now among the most exciting opportunities in the global biotech sector. GenBio AI Careers is offering a cutting-edge Bioinformatics Data Engineer role in Abu Dhabi, providing a unique job in the UAE for professionals passionate about biological data and AI-driven discovery. This Job opportunity for life science graduates allows candidates to work with large-scale multi-omics datasets and advanced computational pipelines while contributing to groundbreaking work in AI-driven digital organisms and next-generation drug discovery.
About GenBio AI
GenBio AI Careers represent a pioneering effort in integrating artificial intelligence with biological sciences. The company focuses on developing multiscale foundation models capable of simulating human biology and accelerating scientific discovery. Through its AI-Driven Digital Organism (AIDO) framework, GenBio AI aims to revolutionize drug discovery, healthcare innovation, and biological simulation. With offices in Palo Alto, Paris, and Abu Dhabi, the company brings together interdisciplinary experts in bioinformatics, machine learning, and computational biology to tackle some of humanity’s most complex scientific challenges.
Job Details:
- Job Title: Bioinformatics Data Engineer
- Location: Abu Dhabi, Abu Dhabi Emirate, United Arab Emirates
Position Overview
The Bioinformatics Data Engineer acts as a bridge between raw biological data and the company’s scalable infrastructure. Reporting to the Data Engineering Lead, the role involves applying biological domain expertise to build scripts and processing logic for complex datasets, ensuring they are prepared for large-scale foundation model training.
Responsibilities
- Source and Acquire Biological Data: Identify, evaluate, and obtain high-quality bioinformatics datasets from public and partner sources such as National Center for Biotechnology Information (NCBI), PubChem chemical database, ENCODE Project Consortium, and UniProt Consortium to support research and model development initiatives.
- Understand Complex Datasets: Develop a comprehensive understanding of biological datasets, including data structures, schemas, metadata standards, entity relationships, and biological context to ensure accurate interpretation and usage.
- Design and Implement Data Processing Pipelines: Develop preprocessing scripts and scalable data transformation workflows using Python, R, and relevant tools. Utilize AI-assisted tools where appropriate to process, clean, normalize, and integrate complex biological data for foundation model training.
- Structure and Standardize Biological Data: Organize heterogeneous datasets into well-defined, interoperable formats aligned with internal infrastructure requirements and downstream AI training pipelines.
- Bioinformatics Data Analysis: Perform exploratory and statistical analysis of genomic, transcriptomic, proteomic, and other multi-omics datasets to assess data quality, identify biological patterns, and generate insights supporting model development. Apply computational and statistical methods to validate assumptions and support AI training and evaluation.
- Build Data Products: Create production-ready data assets such as standardized datasets, curated releases, dashboards, analytical reports, and technical documentation to support research and model evaluation.
- Ensure Data Quality and FAIR Compliance: Curate, annotate, validate, and standardize datasets in accordance with the FAIR data principles (Findable, Accessible, Interoperable, Reusable) to ensure long-term usability and reproducibility.
- Collaborate Cross-Functionally: Work closely with research scientists and machine learning engineers to translate biological research needs into scalable data engineering solutions that support AI model training and evaluation.
- Knowledge Sharing and Documentation: Document methodologies, maintain technical documentation, and share biological data insights across teams.
Qualifications Required for Bioinformatics Job
- Educational Background: Bachelor’s or Master’s degree in Bioinformatics, Computational Biology, or a related field with a strong life sciences focus.
- Biological Data Expertise: Hands-on familiarity with biomedical data modalities, including genomics, transcriptomics, spatial omics, protein structure data, biomedical imaging, and clinical or phenotypic datasets.
- Bioinformatics Tools: Familiarity with tools and platforms such as Bioconda, Biopython, Bioconductor, samtools, bamtools, bcftools, and gffutils.
- Programming and Tooling: Strong programming skills in Python (including libraries such as Pandas and NumPy). Proficiency with bioinformatics workflow managers and tools such as Ray and Kubeflow.
- Engineering Collaboration: Experience writing clean, modular code that can be easily adopted by data engineering teams for optimization in cloud environments such as Amazon Web Services or Google Cloud Platform, and in containerized setups such as Docker container platform.
- AI/ML Awareness: Understanding of machine learning workflows and how biological data must be formatted and batched for deep learning frameworks such as the PyTorch machine learning framework.
