Research Engineer, Data Infrastructure

ABOUT MISTRAL

Mistral provides full-stack AI solutions: from frontier models to developer tools, applications, and compute. We partner with enterprises tackling the hardest problems—across high-stakes industries like finance, manufacturing, defense, healthcare, and the public sector—co-creating customized AI systems that they can run on their terms.

We are a dynamic, collaborative team passionate about AI and its potential to transform society. Our diverse workforce thrives in competitive environments and is committed to driving innovation. Our teams are distributed between Europe, North America, Asia and the Middle East. We are creative, low-ego and team-spirited.

The Role

This role focuses on building and operating the next generation of data infrastructure at Mistral AI. You will be a core contributor to our evolution, helping us design and scale massive compute fleets and storage systems designed for high performance and scalability. You will help us move toward a future of decoupled control and data planes, scaling big data compute and storage platforms while ensuring secure and governed data access for MLOps and research. You will take full lifecycle ownership: from architecting the migration away from legacy orchestrators to implementing production-grade pipelines and participating in on-call rotations for critical training jobs.

What You Will Do

• Build & Scale: Help us reach our goal of operating massive distributed compute and storage systems

• Global Orchestration: Architect and maintain multi-cluster orchestration layers to optimize workload placement across diverse hardware and regions.
• Design Future-Proof Storage: Architect our transition to modern storage formats to handle fine-tuning datasets at a scale that anticipates exabyte growth.
• Platform Engineering: Contribute to the development of our internal training platform, ensuring seamless model training and fine-tuning capabilities across Kubernetes and SLURM based environments.
• Metadata & Lineage: Implement and manage systems to provide clear visibility and lineage as our data and model pipelines grow in complexity.
• Operational Excellence: Use modern deployment workflows to manage cloud-native deployments, ensuring our data platform can scale by orders of magnitude while remaining reliable and efficient.