
WhiteLab Genomics provides an AI-driven platform that speeds discovery and design of genomic therapies, reducing time and experimental workload. It combines graph knowledge technology, machine learning, and advanced computational biology to perform in-silico design and optimization of vectors, payloads, genotoxicity assessment, and experimental protocols. The platform supports modalities including AAV, lentiviral, and nanoparticle delivery and is used by biopharmaceutical companies and research institutions. WhiteLab's tools integrate into genomic R&D workflows to de-risk early development and accelerate candidate selection for downstream preclinical testing.

WhiteLab Genomics provides an AI-driven platform that speeds discovery and design of genomic therapies, reducing time and experimental workload. It combines graph knowledge technology, machine learning, and advanced computational biology to perform in-silico design and optimization of vectors, payloads, genotoxicity assessment, and experimental protocols. The platform supports modalities including AAV, lentiviral, and nanoparticle delivery and is used by biopharmaceutical companies and research institutions. WhiteLab's tools integrate into genomic R&D workflows to de-risk early development and accelerate candidate selection for downstream preclinical testing.
What they do: AI-driven platform (ALFRED AI) for design and optimization of genomic medicines (AAV, lentiviral, non-viral vectors, payloads, bioproduction)
Founded: 2019
Headquarters / presence: Paris, Boston, Montréal
Funding: $10M seed (Sep 12, 2022); pre-seed tied to Y Combinator (Mar 2022)
Investors / partners: Debiopharm Group, Omnes Capital, Y Combinator
| Company |
|---|
Accelerating and de-risking genomic medicine R&D (gene and cell therapy development).
2019
Biotechnology
Pre-seed round associated with Y Combinator
10000000
Seed round announced Sep 12, 2022
“Debiopharm Group and Omnes Capital are lead investors; Y Combinator participated in earlier round”
Computational Biology team plays a key role in the analysis of multi-omic datasets, with a focus on single-cell RNA and DNA analysis, to support our research efforts to improve drug design in gene and cell therapy. Moreover, the computational biologist actively participates in the development of WLG’s In-silico tools, pipelines and models, notably for payload design and manufacturing process optimization. The Data Scientist will work under close supervision, receiving clear instructions and guidance from senior scientists, the Principal Scientist and the Head of Computational Biology.
Our Computational Biology Team plays a key role in the development of predicting and optimization tools for payload design and manufacturing in the context of gene and cell therapy. We perform analysis of multi-omic datasets, with a focus on single-cell RNA and DNA data, to support our research efforts to improve drug design in gene and cell therapy. Moreover, the computational biologist actively participates in the development of WLG’s In-silico tools, pipelines and models.
We are looking for highly skilled and collaborative individuals who are naturally curious, have a passion for learning and solving complex problems with a “can-do” mindset.
As a Data Scientist, here’s how you will make an impact: You will
Nice to have
Design and run new models for Whitelab Genomics’ projects, with a particular focus on bioprocess optimization.
Contribute to the development of new Machine Learning models implementations, features and applications of WhiteLab Genomics’ gene and cell therapy platform.
Help in the development and knowledge of extraction from several databases.
Support the development of biological data analysis pipelines.
Collaborate closely with cross-functional teams.We’re eager to meet you if you have
Recent PhD or master’s in data science, computational biology, bioinformatics, biostatistics, or a related field
1–4 years of experience in Data Science and ML/DL in bioinformatics, biotech or biomedical context
Proficient use of Python and bash and the standard machine learning libraries (scikit-learn, TensorFlow, PyTorch, etc…)
Excellent communication skills, with the ability to effectively convey complex concepts to a non-technical audience