Applied Science - Time Series

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. 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.

Responsibilities

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; Manage research projects and communications with client research teams

Requirements

Fluent in English with excellent communication skills; Expert with PyTorch or JAX; Solid knowledge of modern deep learning and large language models: architectures, pre-training, post-training and evaluation; Experience pre-training or fine-tuning time-series foundation models or large transformer models at scale; Strong background in time-series modelling: probabilistic forecasting, multivariate series and covariates, and classical baselines (statistical models, gradient boosting); 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 is comfortable with uncertainty; 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 (Machine Learning, Statistics, Applied Mathematics, Physics); Research on time-series foundation models: pre-training, scaling, synthetic data or evaluation; Hands-on experience training or fine-tuning LLMs, building agents and tool use, or models that combine text and time series; Industry experience in energy, industrial operations, telecom or finance, or experience deploying models for industrial clients; 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

Benefits

Healthcare coverage; parental leave; retirement plans; relocation support; wellness programs; meal and transportation allowances; other location-specific perks (vary by country)