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! Responsibilities: • 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 Skills/Must Have: • 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 InfiniBand, RoCE, and high-performance distributed training fabrics • Deep understanding of distributed AI training technologies, including NCCL, CUDA, PyTorch Distributed, FSDP, DeepSpeed, 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/namespaces, 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 Desirable Skills: • 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 Benefits: • Huge stock options • Company bonus • Unlimited PTO • 401K + 4% match Salary: • $350,000 base salary