Applied Scientist - State-of-the-Art AI & Formal Methods (EMEA)

Mistral; Paris; Hybrid; EUR 90,000 - 130,000; Full time

Benefits offered by this job

Healthcare coverage

Parental leave

Retirement plans

Relocation support

Wellness programs

Job summary

Mistral AI seeks Applied Scientists and Research Engineers focused on formal methods to design, train, and deploy state-of-the-art language models. You will work with internal and external science, engineering, and product teams to deliver high-impact AI solutions.

You’ll run training pipelines, build data generation tools, and contribute to a large codebase while collaborating across disciplines to solve complex use cases and communicate research progress to client teams.

Qualifications

• Fluent in English and able to explain complex concepts to varied audiences.
• Expert with PyTorch or JAX and large language models.
• Able to navigate a big codebase with minimal guidance.
• Writes clean, high-performance Python code.
• Works well independently in uncertain environments.
• Low-ego, collaborative, eager to learn.
• Proven track record from projects or academia.

Responsibilities

• Run pre-training, post-training and deploy state-of-the-art models on large clusters.
• Generate and curate datasets for formal methods, and build evaluators and benchmarks.
• Develop tools to facilitate data generation, model training, evaluation and deployment.
• Collaborate with cross-functional teams to tackle complex use cases.
• Manage research projects and communications with client research teams.

Skills

English communication

Python development

Independent work

Collaboration

Proven track record

Education

PhD / Master in CS/Math/ML

Tools

PyTorch

JAX

Job description

Mistral AI seeks Applied Scientists and Research Engineers focused on formal methods to design, train, and deploy state-of-the-art language models. You will work with internal and external science, engineering, and product teams to deliver high-impact AI solutions.

You’ll run training pipelines, build data generation tools, and contribute to a large codebase while collaborating across disciplines to solve complex use cases and communicate research progress to client teams.