AI Reliability Engineer, HPC

Reliability & Availability: Ensure uptime, resiliency, and fault tolerance of HPC clusters powering MAI model training and inference. Observability: Design and maintain monitoring, alerting, and logging systems to provide real-time visibility into all aspects of HPC systems including GPU, clusters, storage and networking. Automation & Tooling: Build automation for deployments, incident response, scaling, and failover in CPU+GPU environments. Incident Management: Lead on-call rotations, troubleshoot production issues, conduct blameless postmortems, and drive continuous improvements. Security & Compliance: Ensure data privacy, compliance, and secure operations across model training and serving environments. Collaboration: Partner with ML engineers and platform teams to improve developer experience and accelerate research-to-production workflows. Embody our Culture and Values. Bachelor's Degree in Computer Science, or related technical discipline AND 4+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR equivalent experience Master's Degree in Computer Science, or related technical discipline AND 2+ years technical experience in Site Reliability Engineering, DevOps, or Infrastructure Engineering OR equivalent experience Experience with Kubernetes, Docker, container orchestration, and CI/CD pipelines for ML training or inference workloads. Experience with public cloud platforms such as Azure, AWS, or GCP, including infrastructure-as-code. Experience with monitoring and observability tools such as Grafana, Datadog, or OpenTelemetry. Programming or scripting experience in Python, Go, or Bash. Experience with distributed systems, networking, storage, and high-performance computing (HPC). Experience operating GPU clusters and workload schedulers for ML/AI workloads. Experience with ML training or inference pipelines. Experience with capacity planning and cost optimization for GPU-based infrastructure.