Applied Scientist / Research Engineer - Emea

Mistral AI Mistral AI · AI Frontier · Paris, France · Solutions

Mistral AI is seeking Applied Scientists and Research Engineers to drive innovative research and collaborate with clients on complex research projects. The role involves developing SOTA models across modalities, applying them to diverse use cases, and delivering high-impact AI solutions. Responsibilities include pre-training, post-training, deployment, data generation/curation, developing tools for training/evaluation/deployment, and working with agents and RAG pipelines. The role also involves managing research projects and client communications.

What you'd actually do

  1. Run pre-training, post-training and deploy state of the art models on clusters with thousands of GPUs. You don’t panic when you see OOM errors or when NCCL feels like not wanting to talk.
  2. Generate and curate data for pre-training and post-training, working on evaluations and making sure the model’s performance beats expectations.
  3. Develop the necessary tools and frameworks to facilitate data generation, model training, evaluation and deployment.
  4. Collaborate with cross-functional teams to tackle complex use cases using agents and RAG pipelines.
  5. Manage research projects and communications with client research teams.

Skills

Required

  • PyTorch or JAX
  • Python
  • English fluency
  • communication skills

Nice to have

  • PhD / master in Mathematics, Physics, Machine Learning
  • research experience (agents, multi-modality, robotics, diffusion, time-series)
  • contributed to a large codebase
  • publications in top academic journals or conferences
  • improving existing code by fixing typing issues, adding tests and improving CI pipelines

What the JD emphasized

  • track record of success through personal projects, professional projects or in academia
  • track record of publications in top academic journals or conferences

Other signals

  • develop SOTA models
  • novel methods
  • high-impact AI solutions
  • run pre-training, post-training and deploy
  • generate and curate data
  • develop tools and frameworks
  • tackle complex use cases using agents and RAG pipelines
  • manage research projects and communications with client research teams