About Mistral
Mistral provides full-stack AI solutions, from frontier models to developer tools, applications, and compute. It partners with enterprises in high-stakes industries to co-create customized AI systems that can be run on their terms.
Responsibilities
•Run pre-training, post-training, and deployment of state-of-the-art time-series foundation models on clusters with thousands of GPUs
•Generate and curate large-scale real and synthetic time-series datasets, and build evaluators and benchmarks for forecasting and other time-series tasks
•Advance Timestral architectures, training recipes, scaling, and 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 clients' existing models
•Develop tools and frameworks for data generation, model training, evaluation, and deployment
•Collaborate with cross-functional teams on complex use cases, including agents combining Timestral with Mistral LLMs
•Manage research projects and communications with client research teams
Requirements
•Fluent English and excellent communication skills; able to explain complex technical concepts to technical and non-technical audiences
•Expert knowledge of PyTorch or JAX
•Solid knowledge of modern deep learning and large language models, including 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, including probabilistic forecasting, multivariate series, covariates, and classical baselines such as statistical models and gradient boosting
•Able to contribute to a large codebase and work independently with little guidance
•Writes clean, readable, high-performance, fault-tolerant Python code
•Works independently without roadmaps and is comfortable with uncertainty
•Collaborative, low-ego, and eager to learn
•Track record of success through personal projects, professional projects, or academia
Nice to have
•Founder or builder experience
•A PhD or Master's in Machine Learning, Statistics, Applied Mathematics, or Physics
•Research on time-series foundation models
•Hands-on experience training or fine-tuning LLMs, building agents and tool use, or models combining text and time series
•Industry experience in energy, industrial operations, telecom, or finance
•Experience deploying models for industrial clients
•Experience with large codebases or large machine learning models at scale
•Contributions to a large open-source or industry codebase
•Publications in top academic journals or conferences
•Experience improving existing code through typing, tests, and CI pipelines
Benefits
Benefits may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks
