FREE ACCESS
5,000–10,000 jobs/day
See all jobs on Scoutfield
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
LLM Scientist, AI for Science
Mistral AI. Develop and optimize large language models, agentic AI, and deep learning frameworks for scientific discovery .
Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in developing and optimizing large language models and deep learning frameworks for scientific discovery, with a strong focus on model optimization, scaling techniques, and collaboration with natural science experts.
Highest-signal resume keywords
Large Language Models (LLMs)Deep Learning FrameworksPyTorchTensorFlowScientific Computing
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Agentic AIModel OptimizationReinforcement LearningSynthetic Data GenerationNumerical MethodsHPCAI/ML ApplicationFine-TuningRLHFLiterature Synthesis
Soft Skills
Team-Player MindsetOperational Problem-SolvingCommunication of Technical Concepts
Industry Keywords
Scientific DiscoveryNatural ScienceOpen-Source AI ToolsScalabilityGeneralization
Tech Stack
Tools & technologiesPyTorchTensorflow
About the role
Key responsibilities & impact- Develop and optimize large language models, agentic AI, and deep learning frameworks for scientific discovery
- Improve LLM capabilities for literature synthesis, autonomous experimentation, and rigorous derivations
- Participate in end-to-end training of LLM-based solutions focused on scalability, generalization, and scientific applicability
- Design workflows to accumulate high-quality scientific data at scale through synthetic or crowdsourced pipelines
- Use and improve reinforcement learning and agentic systems for natural-science challenges
- Collaborate with natural-science domain experts to identify AI opportunities and refine models based on feedback
- Contribute to infrastructure for scientific model training and open-source AI tools for science
- Set a research agenda for AI in science and explore high-risk/high-reward ideas
- Translate AI advancements into practical scientific impact
Requirements
What you’ll need- Strong background in LLMs, deep learning, or agentic AI
- Track record of applying AI/ML to real-world problems through publications, open-source contributions, or deployed systems
- Extensive experience in computer science
- Ideally, experience scaling synthetic data generation
- Familiarity with the latest advancements in LLM architectures and post-training
- Hands-on experience with PyTorch, TensorFlow, or JAX
- Experience with model optimization and scaling techniques, including fine-tuning and RLHF
- Ability to understand at least one natural science domain at a high level
- Graduate-level specialization and general undergraduate knowledge in a natural science domain
- Proficiency in scientific computing, including numerical methods and HPC
- Software engineering best practices
- Experience translating technical AI concepts for non-experts
- Low-ego, team-player mindset and willingness to tackle operational challenges
- Ability to work from the Paris office approximately 3 days per week
Benefits
Comp & perks- Healthcare coverage
- Parental leave
- Retirement plans
- Relocation support
- Wellness programs
- Meal allowances
- Transportation allowances
- Other location-specific perks