Site Reliability Engineer - AI Infrastructure

Looking for a role with plenty of growth opportunities?

Join a rapidly growing AI infrastructure provider delivering large-scale compute solutions for AI training and inference across global cloud and GPU environments. The organization partners with leading AI companies and infrastructure providers to build reliable, high-performance platforms supporting next-generation AI workloads.

This opportunity is for a Staff Site Reliability Engineer to lead the reliability of large-scale GPU infrastructure, covering deployment, GPU health, distributed training, networking, and incident response. Working as a senior technical leader, the role focuses on improving infrastructure reliability, operational standards, and platform performance across complex AI environments.

Ready to make a move? Get in touch and apply today!

• Lead high-priority incident response across distributed GPU infrastructure environments
• Diagnose and resolve issues across the stack, including PyTorch, NCCL, CUDA, drivers, networking fabrics, and hardware layers
• Own day-to-day operational health of large-scale GPU fleets, including lifecycle management, validation, firmware rollouts, upgrades, and repair workflows
• Build and maintain observability systems, GPU telemetry platforms, automated remediation tooling, and health-check frameworks
• Define and scale operational practices, including on-call rotations, escalation processes, incident response, and postmortem standards
• Partner closely with infrastructure, product, and platform engineering teams to improve reliability and scalability
• Participate in customer-facing technical discussions, incident reviews, architecture workshops, and workload planning sessions
• Influence physical infrastructure design, including rack layouts, power density, cooling strategies, burn-in procedures, and network topology decisions
• Mentor engineers across reliability engineering, systems operations, and incident management practices
• Contribute to a long-term reliability strategy for hyperscale AI infrastructure environments

• Multiple years of hands-on experience operating large-scale GPU infrastructure environments
• Proven Staff-level SRE or infrastructure engineering experience supporting mission-critical production systems
• Deep expertise with NVIDIA GPU platforms including H100, H200, B200, or GB200 systems
• Strong understanding of GPU memory hierarchy, ECC behaviour, NVLink, NVSwitch, thermal management, and hardware failure analysis
• Production experience with Infini Band, RoCE, and high-performance distributed training fabrics
• Deep understanding of distributed AI training technologies, including NCCL, CUDA, PyTorch Distributed, FSDP, Deep Speed, and Megatron
• Strong software engineering skills in Go, Python, or Rust
• Experience building production-grade automation, tooling, fleet management systems, or reliability platforms
• Hands-on experience with Kubernetes GPU environments, Slurm, or HPC schedulers
• Strong Linux systems expertise, including kernel tuning, CUDA lifecycle management, cgroups/name spaces, BPF/performance analysis, and firmware operations
• Calm, structured incident response capabilities within high-pressure production environments
• Ability to communicate effectively with highly technical customers, providers, and executive stakeholders

• Experience building custom GPU fleet health systems or fabric controllers
• Expertise with distributed storage systems such as VAST, Weka, Lustre, or GPFS
• Experience optimizing distributed training efficiency, checkpointing, and multi-thousand-GPU job performance
• Background supporting enterprise AI infrastructure customers in customer-facing technical roles
• Open-source contributions within the GPU, Kubernetes, or AI infrastructure ecosystem
• Public speaking, technical writing, or community leadership within AI infrastructure or HPC domain

• Huge stock options
• Company bonus
• Unlimited PTO
• 401K + 4% match

• $350,000 base salary