Applied Scientist - Research Engineer (Tabular Data), EMEA

About this role

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. 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.