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Mistral is building full-stack AI solutions, from frontier models to developer tools and applications. The Research Engineering team bridges research and production, spanning Platform (shared infrastructure and tooling) and Embedded (research squads) tracks.
As a Research Engineer on the ML track, you'll optimize large-scale learning systems powering Mistral's open-weight models. You can join either the Platform RE team—enhancing the shared training framework, data pipelines, and cluster tooling used across all teams—or an Embedded RE team within a research squad (Alignment, Pre-training, Multimodal, Safety, etc.) to turn cutting-edge ideas into scalable, repeatable code.
Key responsibilities include accelerating researchers by building robust tools for large-scale ML pipelines; interfacing research with production by integrating checkpoints, streamlining evaluation, and exposing APIs; conducting experiments on deep-learning techniques (sparsified 70B+ runs, distributed training on thousands of GPUs); designing, implementing, and benchmarking ML algorithms in Python; and delivering prototypes that become production-grade components for Le Chat and the enterprise API.
You'll need a Master's or PhD in Computer Science (or equivalent proven track record) and 4+ years working on large-scale ML codebases. Hands-on experience with PyTorch, JAX, or TensorFlow is essential, along with comfort in distributed training frameworks (DeepSpeed, FSDP, SLURM, K8s). Deep learning, NLP, or LLM experience is required; CUDA or data-pipeline expertise is a bonus. Strong software-design instincts—testing, code review, CI/CD—and a collaborative, self-starter mindset are critical.
