ML Research Engineer: Build Scalable DL Pipelines

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

Avantages offerts par ce poste

Healthcare coverage

Relocation support

Parental leave

Meal and 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 collaborate with Research Scientists and move across research-to-production boundaries as needs evolve.

Responsibilities include designing distributed training workflows, implementing robust ML components in Python, and delivering prototypes that scale to enterprise deployments across APIs and platforms.

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.
• Experience in deep learning, NLP or LLMs; CUDA or data-pipeline chops a plus.
• Strong software-design instincts: testing, code review, CI/CD.
• Self-starter, low-ego, collaborative.

Responsabilités

• Accelerate researchers by taking on the 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 70 B + runs, distributed training on thousands of GPUs).
• Design, implement and benchmark ML algorithms; write clear, efficient code in Python.
• Deliver prototypes that become production-grade components for Le Chat and our enterprise API.

Connaissances

PyTorch

JAX

TensorFlow

distributed training

NLP/LLMs

software design

CI/CD

self-starter

collaborative

Formation

Master’s or PhD in Computer Science (or equivalent proven track record)

Outils

DeepSpeed

FSDP

SLURM

Kubernetes (K8s)

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 collaborate with Research Scientists and move across research-to-production boundaries as needs evolve.

Responsibilities include designing distributed training workflows, implementing robust ML components in Python, and delivering prototypes that scale to enterprise deployments across APIs and platforms.