ML Research Engineer — Scale Training Systems

Mistral-Ai; Paris; Sur place; EUR 90 000 - 130 000; Plein temps

Avantages offerts par ce poste

Healthcare coverage

Parental leave

Relocation support

Wellness programs

Meal allowances

Transportation allowances

Résumé du poste

Mistral is seeking a Research Engineer – ML track to build and optimize large-scale learning systems powering open-weight models. You will work with Research Scientists, joining either the Platform RE Team or Embedded RE Team to turn ideas into scalable code.

Responsibilities include accelerating researchers, interfacing research with production, and conducting experiments on state-of-the-art deep-learning techniques.

Qualifications

• Master’s or PhD in Computer Science (or equivalent proven track record).
• 4+ years working on large-scale ML codebases.
• Hands-on with PyTorch, JAX or TensorFlow; distributed training experience (DeepSpeed / FSDP / SLURM / K8s).
• Experience in deep learning, NLP or LLMs; bonus for CUDA or data-pipeline chops.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.

Responsabilités

• Accelerate researchers by handling heavy parts of large-scale ML pipelines and building robust tools.
• Interface cutting-edge research with production: integrate checkpoints, streamline evaluation, and expose APIs.
• Conduct experiments on the latest deep-learning techniques (sparsified 70B+ runs, distributed training).
• Design, implement and benchmark ML algorithms; write clear, efficient Python code.
• Deliver prototypes that become production-grade components for Le Chat and our enterprise API.

Connaissances

PyTorch

JAX

TensorFlow

Distributed training

NLP/LLMs

CI/CD

Testing

Code review

Collaboration

Formation

Master’s or PhD in Computer Science

Outils

SLURM

Kubernetes

DeepSpeed

FSDP

CUDA

Description du poste

Mistral is seeking a Research Engineer – ML track to build and optimize large-scale learning systems powering open-weight models. You will work with Research Scientists, joining either the Platform RE Team or Embedded RE Team to turn ideas into scalable code.

Responsibilities include accelerating researchers, interfacing research with production, and conducting experiments on state-of-the-art deep-learning techniques.