Applied Science - Time Series
Mistral AI is hiring an Applied Science - Time Series in Paris, France. Level rates it ; you can apply on Mistral AI.
Level's read on this job
- Level rates this role Builds AI ●●●●: the job is building AI systems.
- Tools and skills the posting cites include PyTorch, JAX and fine tuning.
- Level lists 225 other open roles at Mistral AI.
- The role was posted in the last few days.
AI in 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.
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.
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.
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). Exceptional candidates from other backgrounds welcome.
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.
What We Offer
We offer a comprehensive benefits package designed to support your well-being, growth, and work-life balance. Benefits vary by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, meal and transportation allowances, and other location-specific perks.
For the most up-to-date details on benefits available in your location, please refer to our Benefits page.
Privacy Policy
Your privacy matters to us. You can learn more about how we handle your personal data in our Applicant Privacy Policy.
How we rate this
Applied Science - Time Series at Mistral AI rates 99 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Questions you could be asked
- Walk me through fine-tuning a model: what data did you use, and how did you check the result?
- Walk me through how you've used PyTorch in your day-to-day work.
- What are the limits of JAX that you've run into, and how did you work around them?
- How would you decide a model or AI system is ready to ship?
- Tell me about a time a model underperformed in production. How did you find out, and what did you change?
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- List these exact terms on your resume: Fine-tuning, PyTorch, and JAX. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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