Mistral AI
Research Engineer, Data Infrastructure
About this role
Mistral is seeking a Research Engineer to design and operate large-scale data infrastructure supporting AI model training and fine-tuning. You'll architect distributed compute and storage systems, manage multi-cluster orchestration, and ensure reliable operations for mission-critical training pipelines.
What you'll do
- Design and scale distributed compute fleets and storage systems for high-performance model training
- Architect multi-cluster orchestration layers optimizing workload placement across hardware and regions
- Build and migrate storage infrastructure toward modern formats handling exabyte-scale fine-tuning datasets
- Develop internal training platform capabilities across Kubernetes and SLURM-based environments
- Implement metadata and data lineage systems for visibility across complex ML pipelines
- Participate in on-call rotations and ensure operational excellence for critical training jobs
What they're looking for
- Python
- Kubernetes and cloud-native tooling
- Distributed systems debugging at scale
- Data infrastructure and MLOps
- Multi-cluster orchestration
- Modern columnar storage formats
- Infrastructure-as-code and deployment workflows
- SLURM or similar batch scheduling systems
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
- Walk us through a time you debugged a critical issue in a large-scale distributed system—what was your approach?
- How would you design a multi-cluster orchestration layer to optimize workload placement across diverse hardware?