Discovery Scientists - AI for Science - Paris

Mistral
ParisPosted 8 May 2026

Job Description

About Mistral At Mistral AI, we believe in the power of AI to simplify tasks, save time, and enhance learning and creativity. Our technology is designed to integrate seamlessly into daily working life. We democratize AI through high-performance, optimized, open-source and cutting-edge models, products and solutions. Our comprehensive AI platform is designed to meet enterprise as well as personal needs. Our offerings include Le Chat, La Plateforme, Mistral Code and Mistral Compute - a suite that brings frontier intelligence to end-users. We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between France, USA, UK, Germany and Singapore. We are creative, low-ego and team-spirited. Join us to be part of a pioneering company shaping the future of AI. Together, we can make a meaningful impact. See more about our culture on https://mistral.ai/careers. Mistral AI participates in the E-Verify program By applying, you agree to our Applicant Privacy Policy. Role Summary We are building a team of exceptional Discovery Scientists, who can identify and solve key problems in their respective fields, here in STEM, with a particular focus on Physics, Chemistry, and Material science. The idea is to leverage the cutting-edge capabilities in AI of our Core Science organization. You will be able to shape the mission and: Accelerate scientific discovery through high quality, open access research Be empowered with advanced AI tools, participate to improving these tools in STEM, and the freedom to explore high-impact projects at the intersection of your domain and AI. Participate in the growth of fundamental and industrial applications in AI for science Generally, our team will focus on two axis: (1) improving the models’ capabilities in solving open science problems and (2) systematically accelerating simulations. What you will do • Identify and Define Impactful Problems: Proactively identify critical challenges in your field where AI/ML can drive breakthroughs. • End-to-End Research Ownership: Lead independent research projects from ideation to implementation, including literature review, hypothesis formulation, numerical experimentation, and validation. • Develop and Apply AI/ML Solutions: Design, implement, and refine advanced algorithms, models, and frameworks (e.g., deep learning, LLMs, agentic AI) to address domain-specific problems. • Collaborate and Innovate: Work with a multidisciplinary team of scientists, engineers, and domain experts (including customers) to push the boundaries of what’s possible with AI in science. • Autonomy and Initiative: Operate with high autonomy, setting your own research agenda and driving projects to completion with minimal oversight. About you • Domain Expertise: Strong understanding and research capabilities in at least one scientific domain (e.g., physics, chemistry or materials science), demonstrated by first-author publications, open-source contributions, or other impactful projects. • Track record in AI/ML; the ability to shed light on hard, real-life problems with an AI solution, demonstrated by further contributions. • Broad experience in coding, for scientific computing and in machine learning, deep learning, and/or large language models. • Hands-on experience using frameworks like PyTorch or TensorFlow is especially appreciated.  • Interdisciplinarity: ability to communicate to a broad-non specialized audience while still remaining technical.  • Customer Collaboration Experience: Experience working with customers or end-users to refine requirements is also appreciated.  • Low ego and team spirit mindset • Embraces hands-on, operational work Motivational paragraph When applying for the AI for Science team at Mistral, please describe in a short paragraph a potential project that you would like to contribute to the team, why it ... (truncated, view full listing at source)
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