Site Reliability Engineer

Mistral•New York, NY

About The Position

Mistral is seeking experienced Site Reliability Engineers to improve the reliability, scalability, and performance of its platform and customer-facing applications. The role balances production operations with long-term software engineering improvements that reduce operational toil and strengthen system reliability, availability, and performance. The SRE will work closely with software engineering and research teams, including AI/ML researchers, to support web services, inference and model-training environments, and workloads across several HPC clusters. The work includes designing and maintaining highly available infrastructure, operating and troubleshooting production systems, improving monitoring and incident response, and developing automation, deployment, and orchestration workflows. The position also involves building a cloud-agnostic platform, supporting reproducible model-training experiments, collaborating on security and compliance, documenting processes, and contributing to open-source projects and technical publications.

Requirements

Master’s degree in Computer Science, Engineering, or a related field.
At least 7 years of experience in a DevOps/SRE role.
Strong experience with cloud computing and highly available distributed systems.
Exposure to site reliability issues in critical environments, including root cause analysis, production troubleshooting, and on-call rotations.
Experience working with reliability KPIs, observability, alerting, and SLAs.
Hands-on experience with CI/CD, containerization, and orchestration tools such as Docker and Kubernetes.
Knowledge of monitoring, logging, alerting, and observability tools such as Prometheus, Grafana, ELK Stack, and Datadog.
Familiarity with infrastructure-as-code tools such as Terraform or CloudFormation.
Proficiency in scripting languages such as Python, Go, or Bash, and knowledge of software development best practices.
Strong understanding of networking, security, and system administration concepts.
Excellent problem-solving and communication skills.
Self-motivated and able to work well in a fast-paced startup environment.

Nice To Haves

Experience in an AI/ML environment.
Experience with high-performance computing (HPC) systems and workload managers such as Slurm.
Experience with modern AI-oriented solutions such as Fluidstack, CoreWeave, or Vast.

Responsibilities

Design, build, and maintain scalable, highly available, fault-tolerant infrastructure for web services and ML workloads.
Maintain availability of the platform, inference, and model-training environments, including enabling replication of work environments across HPC clusters.
Operate production systems and troubleshoot issues, including interrupts, on-call responses, user administration, data extraction, and infrastructure scaling.
Implement and improve monitoring, alerting, and incident response systems.
Implement and maintain workflows and tools for CI/CD, containerization, orchestration, monitoring, logging, and alerting across client-facing APIs and large training runs.
Participate occasionally in on-call rotations, respond to incidents, and perform root cause analysis.
Improve infrastructure automation, deployment, and orchestration using tools such as Kubernetes, Flux, and Terraform.
Collaborate with AI/ML researchers to develop solutions for safe and reproducible model-training experiments.
Build a cloud-agnostic platform that provides an abstraction layer between science and infrastructure.
Design and develop workflows and tooling that improve system reliability, availability, and performance, including automation scripts, refactoring, API features, web apps, and dashboards.
Collaborate with the security team to ensure infrastructure follows security best practices and compliance requirements.
Document processes and procedures to support consistency and knowledge sharing.
Contribute to open-source projects, research publications, blog articles, and conferences.