Senior AI Scientist – Lead AI Scientist

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OWKIN

Site WebLinkedInTous les Emplois

201 - 500 employés

Fondée en 2016

🤖 Intelligence artificielle

🧬 Biotechnologie

💊 Pharmaceutique

Artificial Intelligence • Biotechnology • Pharmaceuticals

Owkin est une entreprise biomédicale orientée par l'intelligence artificielle qui développe un « scientifique IA » autonome (K Pro) pour automatiser et soutenir la recherche et le développement biopharmaceutiques ainsi que la recherche clinique. Sa plateforme combine des modèles avancés d'apprentissage automatique et des modèles fondamentaux formés sur un large réseau de données multimodales de patients, avec une validation en laboratoire humide et des partenariats cliniques pour générer des perspectives biologiques, orienter les essais cliniques et les décisions de patients/populations, et accélérer la découverte de médicaments et les diagnostics. Owkin incube aussi des spin-offs diagnostiques et fournit des outils et services pour les partenaires de données, le développement de modèles, et la recherche translationnelle.

Description

• Architect Agentic Systems: Design and implement sophisticated AI agents capable of multi-step reasoning, tool-use, and self-correction. • Bridge AI and Wet-Lab: Develop frameworks where agents can propose hypotheses and design "optimal experiments" to be validated in the lab. • Scale the "Hacker" Approach: Rapidly prototype and iterate on agentic architectures, moving from paper to production-grade code with speed and precision. • Scientific Leadership: Maintain a watch on the frontier of GenAI and Agentic workflows, publishing high-impact research at the intersection of AI, Biology and Chemistry.

🎯 Exigences

• Demonstrated Expertise in Agentic AI: Deep understanding of LLM reasoning (Chain-of-Thought, ReAct), long-context management, and tool-augmented generation. • The Hacker Toolkit: Expert-level Python skills with a focus on "building." You should be comfortable writing clean, modular code that interfaces with complex APIs and databases. • Publication Record: History of contributions to top-tier ML conferences (NeurIPS, ICML, ICLR). • Technical Foundation: PhD in Computer Science, Machine Learning, or Applied Mathematics (or equivalent high-level industry experience). • Communication: Ability to explain complex agent behaviors to both AI peers and biological experts. Excellent written and oral communication skills. Fluent in English (French is a plus). • Team first, highly collaborative mindset. • Computational Biology or Chemistry expertise is a big plus. • Domain Knowledge: Deep understanding of the "central dogma" data stack—from processing raw sequencing (omics) to interpreting the physics and geometry of macromolecular structures. • Closed-loop Discovery: Experience in Active Learning, Bayesian Optimization, or Optimal Experimental Design. • Systems Engineering: Experience training and deploying large-scale models or managing complex multi-agent orchestrations. • Causal Inference: Experience in understanding "response to treatment" or causal discovery in biological systems. • Industry Impact: Proven track record of taking a research concept and turning it into a functional tool used by other scientists.

🏖️ Avantages

• Flexible work organization • Friendly and informal working environment • Opportunity to work with an international team with high technical and scientific backgrounds