ml engineer for time-series foundation models

Описание:

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.

Задачи:

• 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

Требования:

• 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

Будет плюсом:

• 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

Условия:

• healthcare coverage
• parental leave
• retirement plans
• relocation support
• wellness programs
• meal and transportation allowances
• other location-specific perks

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