Mistral AI
Site Reliability Engineer
About this role
Mistral seeks an experienced Site Reliability Engineer to ensure the reliability, scalability, and performance of its AI platform and customer-facing applications in New York. You'll balance production operations with long-term infrastructure improvements, supporting both web services and ML workloads across distributed HPC clusters.
What you'll do
- Design and maintain scalable, highly available infrastructures for web services and ML workloads
- Operate production systems, troubleshoot issues, and respond to on-call incidents with root cause analysis
- Implement and improve monitoring, alerting, and incident response systems to minimize downtime
- Build and maintain CI/CD pipelines, containerization, and orchestration workflows for APIs and training runs
- Drive infrastructure automation using Kubernetes, Flux, and Terraform to reduce operational toil
- Collaborate with ML researchers to enable safe, reproducible model-training experiments and develop cloud-agnostic platform abstractions
What they're looking for
- Kubernetes and container orchestration
- Infrastructure-as-Code (Terraform, CloudFormation)
- CI/CD pipeline design and implementation
- Monitoring and observability tools (Prometheus, Grafana, ELK Stack, Datadog)
- Scripting languages (Python, Go, Bash)
- Distributed systems and high availability architecture
- Networking, security, and system administration
- On-call incident response and troubleshooting
Benefits
- Work on frontier AI systems solving high-stakes problems in finance, manufacturing, defense, and healthcare
- Collaborate with diverse, creative teams across Europe, North America, Asia, and the Middle East
- Opportunity to contribute to open-source projects and publish research
- Dynamic startup environment focused on innovation
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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 your experience managing infrastructure for ML workloads—what were the unique reliability challenges you faced?
- Walk us through a significant production incident you handled: what went wrong, how you debugged it, and what preventive measures you implemented?