Job Specifications
À propos
Scienta is a deeptech company advancing translational research in immunology.
We develop EVA, the first AI foundation model purpose-built for translational prediction in immune-mediated diseases. EVA addresses one of the biggest challenges in pharma R&D: the preclinical–clinical gap, where fewer than 10% of immune drug programs successfully translate to humans. Our mission is to bring predictive clarity to this high-risk phase of development.
Built on more than 3 billion multimodal data points across 30 diseases and 70 tissues, EVA is designed to reason like an immunologist. Its biology-first architecture translates early experimental signals into predicted patient outcomes, with explainable outputs such as pathway-level activity and mechanism-linked biomarkers that directly inform R&D decisions.
Our approach is scientifically validated. In inflammatory bowel disease, EVA accurately replicated anti-TNF clinical trial outcomes using only preclinical and baseline transcriptomic data, outperforming existing academic and private models. Our work is backed by 18 peer-reviewed publications and 2 patents.
Located at BioLabs Hôtel-Dieu (Paris), Scienta is supported by leading investors, was selected among Station F’s Future 40, and named a laureate of the EIC Accelerator. Our team brings together AI, immunology, and pharma expertise, with a strong PhD backbone.
If you want to work on hard scientific problems with real impact, you’ll feel at home at Scienta!
Descriptif du poste
As a Senior AI Research Scientist specializing in generative models at Scienta, you will lead the development of next-generation AI approaches to model and generate biological data for drug discovery applications. You will spearhead research into novel generative architectures that can synthesize realistic biological scenarios, predict drug responses, and generate hypothetical therapeutic interventions in immune-inflammatory diseases.
Working at the intersection of cutting-edge AI and computational biology, you will expand our EVA foundation model capabilities by incorporating advanced generative techniques. Your research will directly contribute to accelerating drug discovery by enabling the simulation of complex biological systems and the generation of novel therapeutic hypotheses You will collaborate with immunologists, computational biologists, and ML engineers in a multi-disciplinary environment, leading high-impact research projects that bridge theoretical advances in generative AI with practical applications in precision medicine.
We value scientific rigor, innovation, and aim to publish breakthrough research in top-tier venues. This position offers exceptional opportunities for leadership, mentorship, and driving the scientific direction of our AI research program.
Main Missions
Lead the development of novel generative architectures (diffusion models, VAEs, GANs, flow-based models) specifically tailored for biological data modalities including transcriptomics, proteomics, and clinical data;
Design and implement generative models that can synthesize realistic patient profiles, drug response scenarios, and disease progression trajectories to augment training datasets and enable what-if analyses;
Lead authorship on high-impact publications in top-tier AI and computational biology venues, represent the company at international conferences and workshops;
Guide junior researchers and collaborate with computational biologists to translate generative AI advances into practical drug discovery applications;
Translate research ideas into practical applications of our foundation model, to impact the way drugs are developed today in immunology and inflammation.
Profil recherché
PhD in Machine Learning, Computer Science, Statistics, or related quantitative field with focus on generative models;
5+ years of research experience with strong publication record in top-tier venues (NeurIPS, ICML, ICLR, Nature Methods, Nature Machine Intelligence, or equivalent);
Expert-level Python programming, extensive experience with PyTorch or JAX, and familiarity with distributed training and GPU optimization.
Informations complémentaires
Type de contrat : CDI
Lieu : Paris
Télétravail partiel possible