The Role
Mistral AI is seeking Applied Scientists and Research Engineers focused on tabular data. You will design, train, and deploy SOTA models that understand, query, and reason over structured data, and apply them to diverse use cases.
Working cross-functionally with both external and internal science, engineering, and product teams you will deliver high-impact AI solutions. We are made of researchers, engineers, product builders, ex-founders who love tackling the most complex real world problems in the industry.
What you will do
Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs.
Generate and curate datasets of tables, spreadsheets and databases, and build evaluators and benchmarks for table understanding, text-to-SQL, and prediction on tabular data.
Develop the necessary tools and frameworks 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.
About you
Fluent in English with excellent communication skills — at ease explaining complex technical concepts to both technical and non-technical audiences.
Expert with PyTorch or JAX.
Experience training or fine-tuning large language models, or tabular foundation models (e.g. TabPFN).
Strong background in machine learning on tabular data, including classical baselines (e.g. gradient boosting), and comfortable with SQL, pandas, and messy real-world data.
Not afraid of contributing to a big codebase; finds their way around independently with little guidance.
Writes clean, readable, high-performance, fault-tolerant Python code.
Doesn’t need roadmaps: moves independently and navigates uncertainty with confidence.
Low-ego, collaborative and eager to learn.
Track record of success through personal projects, professional projects or academia.
Nice to have
Ex-founder or builder
PhD / Master in a relevant field (Computer Science, Statistics, Machine Learning) — exceptional candidates from other backgrounds welcome.
Experience with text-to-SQL, table question answering, or spreadsheet agents.
Experience with reinforcement learning using verifiable rewards (e.g. query execution).
Experience working on large code bases or large machine learning models at scale.
Contributions to a large open-source or industry codebase.
Publications in top academic journals or conferences.
Enjoys improving existing code: typing, tests, CI pipelines.