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Mistral AI

Applied Scientist - Research Engineer (Tabular Data), EMEA

Mistral AI is hiring an Applied Scientist - Research Engineer (Tabular Data), EMEA for a remote role open to applicants in France. Level rates it ; you can apply on Mistral AI.

Level's read on this job

  • Level rates this applied scientist role Builds AI ●●●●: the job is building AI systems.
  • The posting lists this role as remote.
  • Tools and skills the posting cites include PyTorch, JAX and fine tuning.
  • Level lists 230 other open roles at Mistral AI.
  • The role was posted in the last few days.

AI in this role

pytorchjax
fine-tuning

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.

The Role

Mistral AI is seeking Applied Scientists and Research Engineers focused on tabular data. You will design, train, and deploy SOTA models that understand, query, and reason over structured data, and apply them to diverse use cases.

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 models on clusters with thousands of GPUs.

  • Generate and curate datasets of tables, spreadsheets and databases, and build evaluators and benchmarks for table understanding, text-to-SQL, and prediction on tabular data.

  • 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.

  • 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.

  • Experience training or fine-tuning large language models, or tabular foundation models (e.g. TabPFN).

  • Strong background in machine learning on tabular data, including classical baselines (e.g. gradient boosting), and comfortable with SQL, pandas, and messy real-world data.

  • 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 navigates uncertainty with confidence.

  • 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 (Computer Science, Statistics, Machine Learning) — exceptional candidates from other backgrounds welcome.

  • Experience with text-to-SQL, table question answering, or spreadsheet agents.

  • Experience with reinforcement learning using verifiable rewards (e.g. query execution).

  • 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 Scientist - Research Engineer (Tabular Data), EMEA at Mistral AI rates 97 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.

Classification

Builds AI. The job is building AI systems.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ 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

Fine-tuningPyTorchJAX

Also common in applied scientist jobs at this level

Pay

Typical applied scientist job
$172k

This posting doesn't list pay. Use the typical applied scientist figure when pay comes up. See applied scientist jobs

Questions you could be asked

  1. Walk me through fine-tuning a model: what data did you use, and how did you check the result?
  2. Walk me through how you've used PyTorch in your day-to-day work.
  3. What are the limits of JAX that you've run into, and how did you work around them?
  4. How would you decide a model or AI system is ready to ship?
  5. Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

  • 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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