LLM Scientist, AI for Science

Mistral
Paris, France
Hybrid

Who this role is best for

Geared toward AI researchers with a strong foundation in natural sciences and experience in LLM optimization, comfortable with cross-disciplinary collaboration and scientific data generation. The role is based in Paris and favors candidates with team-oriented, low-ego mindsets.

Best fit for

  • Candidates with interdisciplinary AI and natural science expertise, particularly in model optimization and synthetic data generation
    — “Strong background in LLMs, deep learning, or agentic AI, with a track record of applying these to real-world problems”
  • Individuals who can translate complex AI concepts into practical tools for non-AI experts
    — “Experience translating technical AI concepts for non-experts (e.g., scientists, engineers)”
  • Researchers who thrive in collaborative, distributed teams and have experience in high-stakes industries
    — “Our teams are distributed between Europe, North America, Asia and the Middle East”

Things to consider

  • The role demands scientific domain knowledge beyond AI, requiring at least graduate-level specialization
    — “Ability to understand 1 Natural Science domain at a high level (e.g., graduate level specialization + general undergrad knowledge)”
  • Candidates should be prepared to work on high-risk, high-reward research agendas
    — “Set a research agenda for AI in science, exploring high-risk/high-reward ideas”

How to stand out

  • Highlight experience in synthetic data generation and model optimization in your resume and interview responses
    — “extensive experience in computer science and ideally experience scaling synthetic data generation”
  • Showcase your ability to refine AI models based on domain scientist feedback in your portfolio or projects
    — “Work closely with domain scientists to identify AI opportunities and refine models based on feedback”
  • Emphasize your familiarity with LLM architectures and post-training techniques in your application
    — “Familiarity with the latest advancements in LLM architectures and post training”
  • Demonstrate hands-on experience with PyTorch/TensorFlow/JAX and model scaling techniques
    — “Hands-on experience with PyTorch/TensorFlow/JAX, model optimization, and scaling techniques”
Pace · VariedCollaboration · HighAutonomy · MediumDecision Impact · Company

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • Developing and optimizing LLMs for scientific applications
  • Contributing to company infrastructure for scientific AI tools
Typical background
Strong background in AI/ML with a focus on LLMs and deep learningExperience in scientific computing and software engineering

Skills & requirements

Required

Large Language Models (llms)Deep LearningReinforcement LearningAgentic AIData CreationAlgorithm DesignModel OptimizationScientific ComputingSoftware Best PracticesCollaboration With Domain Experts

Preferred

Experience Scaling Synthetic Data GenerationFamiliarity With HPCProficiency In Pytorch/tensorflow/jax

Stack & domain

LlmsDeep LearningReinforcement LearningAgentic SystemsPyTorchTensorFlowJAXModel OptimizationScaling TechniquesFine-tuningRLHFCollaborationCommunicationProblem-solvingTeamworkAIScienceNatural SciencesBiologyChemistryPhysicsMaterials Science

About the role

Original posting from Mistral via Ashby

ABOUT MISTRAL

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector, co-creating customized AI systems that they can run on their terms.

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 Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

ABOUT THE ROLE

We are hiring an LLM Scientist to push the boundaries of AI-driven scientific discovery. You will specialize in developing and optimizing large language models (LLMs), agentic AI, and deep learning frameworks to solve open problems in the natural sciences: Biology, Chemistry, Physics, and/or Materials Science. Your work will empower scientists with cutting-edge AI tools and accelerate research through:

  • Model-centric innovation: Improving LLMs’ capabilities to tackle complex scientific problems (e.g., literature synthesis, autonomous experimentation, rigorous derivations).
  • Cross-disciplinary collaboration: Partnering with Natural sciences domain experts to translate AI advancements into real-world scientific impact.

WHAT YOU WILL DO

  • AI/ML Research: Participate in end-to-end training of LLM-based solutions, with a focus on scalability, generalization, and scientific applicability.
  • Data creation: Design original workflows to accumulate high quality scientific data at scale, either through synthetic or crowd sourced pipelines.
  • Algorithm Design: Use and improve reinforcement learning, or agentic systems tailored to Natural sciences challenges (e.g., modeling resolution, material property prediction, advanced data analysis and coding).
  • Interdisciplinary Bridge: Work closely with domain scientists to identify AI opportunities and refine models based on feedback.
  • Tooling & Infrastructure: Contribute to company infrastructure to improve usability for model training in scientific contexts and open-source AI tools for science for non-AI experts.
  • Autonomous Innovation: Set a research agenda for AI in science, exploring high-risk/high-reward ideas.

WHAT WE'RE ARE LOOKING FOR

  • AI/ML Expertise: Strong background in LLMs, deep learning, or agentic AI, with a track record of applying these to real-world problems (publications, open-source contributions, or deployed systems) – at the minimum extensive experience in computer science and ideally experience scaling synthetic data generation.
  • State-of-the-art Knowledge: Familiarity with the latest advancements in LLM architectures and post training.
  • Technical Depth: Hands-on experience with PyTorch/TensorFlow/JAX, model optimization, and scaling techniques (e.g., fine-tuning, RLHF).
  • Scientific Capability: Ability to understand 1 Natural Science domain at a high level (e.g., graduate level specialization + general undergrad knowledge) to design effective AI solutions.
  • Engineering Rigor: Proficiency in scientific computing (e.g., numerical methods, HPC) and software best practices.
  • Collaboration: Experience translating technical AI concepts for non-experts (e.g., scientists, engineers) and iterating based on user needs.
  • Mindset: Low ego, team player, and eager to tackle operational challenges (e.g., data pipelines, model deployment).

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 is challenging and interesting in general, and more specifically for Mistral. Please also highlight the specific skill set that you can bring to the team.

WHAT WE OFFER

We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.

For the most up-to-date details on benefits available in your location, please refer to our Benefits page https://app.notion.com/p/mistralai/Benefits-at-Mistral-36e6ba59a7fe836b93dd01737fcc27ef?source=copy_link.

PRIVACY POLICY

Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy https://legal.mistral.ai/terms/applicant-privacy-policy.

Source: Mistral careers (Ashby)

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