Applied Science - Time Series

Job description

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 Job Mistral AI is seeking Applied Scientists and Research Engineers focused on time series. You will design, train, and deploy state-of-the-art time-series foundation models, and apply them to the most demanding forecasting problems in industry, from power grids and plants to trading desks. Timestral, our family of time-series foundation models, ranked first on amongst all benchmarks at release ( tech report ). The role has two sides: building the next generations of Timestral with our Science team, and taking them into production with our industrial and financial clients, adapted to their data. 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 time-series foundation models on clusters with thousands of GPUs. Generate and curate large-scale time-series datasets, real and synthetic, and build evaluators and benchmarks for forecasting and other time-series tasks. Push the frontier of Timestral: architectures, training recipes, scaling, and new capabilities such as anomaly detection, classification and embeddings. Fine-tune and adapt Timestral on client data, in the cloud or on-premise, and benchmark it against the client's existing models. 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, including agents that combine Timestral with Mistral LLMs. Man…