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Mistral provides full-stack AI solutions spanning frontier models, developer tools, applications, and compute infrastructure. The company partners with enterprises across high-stakes industries including finance, manufacturing, defense, healthcare, and the public sector to co-create customized AI systems.
You will join a dynamic, distributed team passionate about AI innovation. This role focuses on formal methods applied to large language models, combining research and engineering to deliver state-of-the-art solutions.
Key responsibilities:
- Run pre-training, post-training, and deploy state-of-the-art models on large clusters
- Generate and curate datasets for theorem proving and software verification
- Build evaluators and benchmarks for formal reasoning capabilities
- Develop tools and frameworks to facilitate data generation, model training, evaluation, and deployment
- Collaborate cross-functionally with internal and external science, engineering, and product teams
- Manage research projects and communications with client research teams
You will work on high-impact problems at the intersection of AI and formal verification, contributing to systems that can reason about code correctness and mathematical proofs.
Requirements:
- Fluent English with excellent communication skills; able to explain complex technical concepts to both technical and non-technical audiences
- Expert proficiency with PyTorch or JAX
- Comfortable navigating large codebases independently with minimal guidance
- Write clean, readable, high-performance, fault-tolerant Python code
- Self-directed; able to move independently and navigate uncertainty with confidence
- Collaborative, low-ego mindset with eagerness to learn
- Demonstrated track record of success through personal projects, professional work, or academic contributions
Nice to have:
- PhD or Master's degree in Computer Science, Mathematics, or Machine Learning
- Experience with autoformalization, proof automation, or verified code generation
- Experience with reinforcement learning using verifiable rewards, ideally from Lean or other proof assistants
- Contributions to large open-source or industry codebases
- Publications in top academic journals or conferences
- Interest in code quality improvements including typing, tests, and CI pipelines
