Bioinformatics Data Engineer | GenBio AI · Teeming.ai
GenBio AI
GenBio AI is building the world's first AI-Driven Digital Organism (AIDO), a system of multiscale foundation models designed to predict, simulate, and program biology across all levels, from…
GenBio AI is building the world's first AI-Driven Digital Organism (AIDO), a system of multiscale foundation models designed to predict, simulate, and program biology across all levels, from…
Your next opportunity is in here somewhere. Sign up to explore 52,000+ startups and their open roles. No spam. No gamification. Just jobs.
52,000+
Startups
65,000+
Open Roles
1,400+
New This Week
Product Designer
Part-timeBelgrade, RS
Part-time • Belgrade, RS
Backend Developer
Full-timeNiš, RS
Full-time • Niš, RS
Frontend Developer
ContractBerlin, DE
Contract • Berlin, DE
Backend Developer
Full-timeHaifa
Full-time • Haifa
AI Researcher
Full-timeTel Aviv
Full-time • Tel Aviv
Backend Developer
InternshipHaifa
Internship • Haifa
GenBio AI develops multiscale foundation models to decode and simulate human biology. Our team is accelerating towards an ambitious future where scientists can unlock humanity's biggest challenges in drug discovery, healthcare, and fundamental research with AIDO (AI-Driven Digital Organism): a unified framework for predicting, simulating, and programming biology across all scales. The foundation of this vision begins today as we engineer the virtual cell to model and simulate the fundamental unit of life.
This vision has brought together a talent-dense group of product-minded researchers and engineers dedicated to bringing it to reality. Our team prides itself on our strong engineering culture and highly interdisciplinary and collaborative approach. We are based in Palo Alto, with satellite offices in Paris and Abu Dhabi.
As our data ingestion needs grow, we are looking for a Bioinformatics Data Engineer to act as the crucial bridge between raw biological data and our scalable infrastructure. Reporting to the Data Engineering Lead, you will leverage your deep biological domain expertise to build the initial scripts and processing logic for complex datasets, ensuring they are primed for large-scale foundation model training.
Responsibilities
Qualifications
Educational Background: Bachelor’s or Master’s degree in Bioinformatics, Computational Biology, Computer Science, or a related field with a heavy focus on the life sciences
Biological Data: Deep, hands-on familiarity with multiple biomedical data modalities (e.g., genomics, transcriptomics, spatial omics, protein structure, biomedical imaging, clinical/phenotypic data, etc.)
Biological Tools: Familiar with Bioconda,Biopython,Bioconductor, samtools,bamtools,bcftools,gffutils etc
Scripting & Tooling: Strong programming skills in Python (Pandas, NumPy) and proficiency with standard bioinformatics workflow managers and tools (e.g., Ray, Kubeflow)
Engineering Handoff: Experience writing clean, modular code that can be easily picked up by core data engineers for optimization in cloud environments (AWS/GCP/HF) and containerized setups (Docker)
Join us as we embark on this journey to redefine the future of biology and medicine.
We are an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. GenBio AI participates in the U.S. Department of Homeland Security’s E-Verify program to confirm the employment eligibility of all newly hired employees. For more information on E-Verify, please visit www.e-verify.gov.
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
Source & Acquire Biological Data - Identify, evaluate, and obtain high-quality bioinformatics datasets from public and partner sources (e.g., NCBI, PubChem, ENCODE,UniProt etc.) to support research and model development initiatives
Deeply Understand Complex Datasets - Develop a comprehensive understanding of biological datasets, including data structures, schemas, metadata standards, entity relationships, and underlying biological context to ensure accurate interpretation and usage
Design & Implement Data Processing Pipelines - Develop robust preprocessing scripts and scalable data transformation workflows using Python, R, and relevant Tools. Leverage AI-assisted tools where appropriate to process, clean, normalize, and integrate complex biological data for foundation model training
Structure & 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, uncover biological patterns, and generate insights that inform model development. Apply appropriate computational and statistical methods to validate assumptions and support downstream AI training and evaluation
Build Data Products - Create production-ready data assets, including standardized datasets, curated releases, dashboards, analytical reports, and technical documentation to enable efficient research and model evaluation
Ensure Data Quality & FAIR Compliance - Curate, annotate, validate, and standardize public and partner datasets in alignment with FAIR (Findable, Accessible, Interoperable, Reusable) principles, ensuring long-term usability and reproducibility
Collaborate Cross-Functionally - Partner closely with research scientists and ML engineers to translate biological research needs into scalable data engineering solutions that support AI model training and evaluation
Knowledge Sharing & Documentation - Contribute domain expertise by documenting data methodologies, maintaining clear technical documentation, and sharing biological data insights across teams
AI/ML Awareness: A solid understanding of machine learning workflows and how biological data must be formatted and batched for deep learning frameworks (e.g., PyTorch)
