Mistral AI
Research Engineer, Machine Learning
About this role
Mistral seeks a Research Engineer to build and optimize large-scale machine learning systems powering open-weight models. You'll work alongside Research Scientists in either Platform (shared infrastructure) or Embedded (research squad) teams, bridging cutting-edge research with production deployment.
What you'll do
- Build and optimize large-scale ML training pipelines and distributed learning systems
- Develop robust tools and infrastructure for researchers to accelerate model development
- Implement and benchmark deep learning algorithms, focusing on PyTorch/JAX efficiency
- Integrate research checkpoints into production, streamline evaluation, and expose APIs
- Conduct experiments on advanced techniques including sparsified 70B+ parameter models on thousands of GPUs
- Deliver prototypes that transition into production components for Le Chat and enterprise APIs
What they're looking for
- Python and software engineering best practices (testing, code review, CI/CD)
- PyTorch, JAX, or TensorFlow with hands-on distributed training experience
- Distributed training frameworks: DeepSpeed, FSDP, SLURM, or Kubernetes
- Deep learning, NLP, or LLM expertise
- CUDA optimization and/or data pipeline engineering
- Large-scale ML codebase development (4+ years)
- ML algorithm design and implementation
- System design and architecture thinking
Benefits
- Healthcare coverage
- Parental leave
- Retirement plans
- Relocation support
- Wellness programs
- Meal and transportation allowances
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Mistral AI
Mistral AI builds large-scale machine learning systems and open-weight AI models, supported by infrastructure powering petabyte-scale HPC clusters and enterprise AI platforms. The company is hiring Systems Engineers, Research Engineers, Site Reliability Engineers, and Applied AI Engineers to scale training infrastructure, optimize data systems, ensure platform reliability, and drive customer adoption across industries.
- Website
- mistral.ai
Likely interview questions
- Describe a time you optimized a large-scale ML training pipeline—what bottlenecks did you identify and how did you resolve them?
- How have you approached debugging distributed training issues across thousands of GPUs?