Senior Site Reliability Engineer

• Architect for Reliability & Scale: Participate in critical re-architecture sessions to redesign our systems for higher efficiency and scale. You won't just maintain existing clusters; you will help define how our next-generation infrastructure operates.
• Own Multi-Cloud GPU Clusters: Take end-to-end ownership of our production clusters for training and inference across AWS and OCI, ensuring high availability and peak performance.
• Drive Security & Compliance: Assist in achieving and maintaining security certifications (SOC 2 Type 1 & 2, ISO standards) by implementing robust infrastructure security practices in a fast-moving AI startup environment.
• Deep Linux Performance Tuning: Use your mastery of Linux systems to troubleshoot and optimize performance at the OS and kernel level.
• Build Robust Automation: Write high-quality tools and automation in Python, Go, or Bash to manage, monitor, and heal our infrastructure without relying on heavy operational toil.
• Debug Complex Hardware/Software Failures: Serve as the final escalation point for the most challenging GPU, networking (InfiniBand/RDMA), and system-level issues, often collaborating directly with hardware vendors like NVIDIA.

• 5+ years of experience as an SRE, production engineer, or infrastructure engineer in a fast-paced, large-scale environment.
• Deep Linux Mastery: You possess deep, hands-on expertise in Linux, containerized systems, and debugging low-level system performance.
• Expert in Technologies: You have working experiencewith Terraform, Airflow, and Ray
• Cloud Infrastructure Expert: You have strong experience with providers like AWS or OCI. 
• Tenacious Troubleshooter: You thrive on solving complex, low-level problems where hardware and software intersect.
• Startup DNA: You are energetic and thrive in a less structured, fast-paced environment.
• Security-Minded: You possess a working knowledge of security best practices and familiarity with compliance frameworks, such as SOC 2 and ISO.
• Expert in High-Performance Networking: You have practical experience with InfiniBand, RDMA, or RoCE and understand how to optimize throughput for massive distributed training jobs.

• Deep expertise with GPU tooling for NVIDIA and AMD GPUs like DCGM or ROCm.
• Experience managing large-scale GPU clusters for AI/ML workloads (training or inference).
• Familiarity with job management systems based on Kubernetes or orchestration frameworks like Ray.
• Deep expertise in Data Pipeline and Infrastructure

Compensation

About Luma