Senior Site Reliability Engineer AI Infrastructure

About this role

Senior Site Reliability Engineer - AI Infrastructure Location: Global Remote / San Francisco · Full-Time About Andromeda Andromeda Cluster was founded by Nat Friedman and Daniel Gross to give early-stage startups access to the kind of scaled AI infrastructure once reserved only for hyperscalers. We began with a single managed cluster — but it filled almost instantly. Since then, we’ve been quietly building the systems, network, and orchestration layer that makes the world’s AI infrastructure more accessible. Today, Andromeda works with leading AI labs, data centers, and cloud providers to deliver compute when and where it’s needed most. Our platform routes training and inference jobs across global supply, unlocking flexibility and efficiency in one of the fastest-growing markets on earth. Our long-term vision is to build the liquidity layer for global AI compute — a marketplace that moves the infrastructure and workloads powering AGI not dissimilar to the flows of capital in the world’s financial markets. We are expanding to new frontiers to find the brightest that work in AI infrastructure, research and engineering. The Role This is not a generalist SRE role. You will design, operate, and debug large-scale GPU infrastructure used for distributed training and inference, working directly with customers pushing the limits of modern AI systems. We’re looking for engineers who have personally run GPU clusters in production, understand the failure modes of distributed training, and can reason about performance from network fabric → kernel → framework. What You’ll Own GPU Cluster Architecture: Design and evolve multi-provider, multi-region GPU compute clusters optimized for large-scale training. Make topology-aware scheduling, networking, and storage decisions that directly impact training throughput and cost efficiency. Customer Technical Partnership: Serve as the primary technical point of contact for customers running large-scale training workloads. Onboard, troubleshoot, and optimize, often in real time. Reliability & Performance Engineering: Define SLOs and error budgets that account for the unique failure modes of GPU infrastructure (ECC errors, NVLink degradation, NCCL timeouts). Own capacity planning across heterogeneous GPU fleets optimized for training throughput. Networking & Fabric Health: Ensure the health and performance of high-speed interconnects (InfiniBand, RoCE, NVLink) that underpin distributed training. Diagnose and resolve fabric-level issues that degrade collective operations. Observability: Build deep visibility into GPU utilization, memory pressure, interconnect throughput, training job performance, and hardware health. Go well beyond standard infrastructure metrics. Automation & Tooling: Build production-grade automation for cluster provisioning, GPU health checks, job scheduling, self-healing, and firmware/driver lifecycle management. Incident Leadership: Lead incident response for complex, multi-layer failures spanning hardware, networking, orchestration, and ML frameworks. Drive blameless postmortems and systemic fixes. What We’re Looking For GPU Systems Expertise: Deep, hands-on experience operating large-scale GPU clusters (NVIDIA A100/H100/B200 or equivalent). You understand GPU memory hierarchies, ECC behavior, thermal throttling, and hardware failure modes from direct experience not documentation. High-Performance Networking: Production experience with InfiniBand, RoCE, or NVLink fabrics in the context of distributed training. You can diagnose why an all-reduce is slow, identify a degraded link in a fat-tree topology, and reason about congestion control at scale. Distributed Training & ML Frameworks: Working knowledge of how large training jobs actually run — NCCL, CUDA, PyTorch distributed, DeepSpeed, Megatron, FSDP, or similar. You don't need to write the models, but you need to understand what's happening at the systems level when a 1,000-GPU training run stalls. Linux & Systems Internals: Expert-level Linux knowledge: kernel tuning, driver management (NVIDIA drivers, CUDA toolkit), cgroup/namespace internals, performance profiling at the syscall and hardware level. Kubernetes & Orchestration: Strong experience running Kubernetes in production with GPU workloads, including device plugins, topology-aware scheduling, multi-cluster federation, and custom operators. Experience with Slurm or other HPC schedulers is equally valued. Automation & Software Engineering: Strong engineering skills in Python, Go, or Bash. You build production-grade tools and services, not just scripts. Infrastructure-as-Code proficiency (Terraform, Helm, Ansible, or equivalent). Observability & Monitoring: Hands-on experience building monitoring and alerting for GPU infrastructure, not just Prometheus/Grafana basics, but GPU-specific telemetry (DCGM, nvidia-smi, fabric manager metrics) integrated into actionable dashboards. Incident Management: Proven track record leading incident response for complex distributed systems where the failure could be in hardware, firmware, networking, drivers, orchestration, or application code and you need to narrow it down fast. Strong Candidates May Have Distributed Storage: Experience with high-performance parallel file systems (VAST, Weka, Lustre, GPFS) and the checkpoint I/O and data-loading bottlenecks that come with large training runs. Training Optimization: Experience profiling and optimizing distributed training performance: identifying stragglers, tuning collective communication strategies, improving MFU (Model FLOPs Utilization), and reducing idle GPU time across large runs. Cluster Buildout & Hardware: Experience involved in physical cluster design - rack layout, power/cooling constraints, network topology design, and hardware validation/burn-in at scale. Team Leadership: Experience leading or mentoring a team of infrastructure engineers. We're growing and need people who raise the bar for everyone around them. Why You’ll Love It Here This is a high-impact, senior builder’s role. You’ll have significant ownership and autonomy to shape how our systems run at a foundational level, working directly with customers and providers while architecting the infrastructure backbone for reliable, scalable AI compute. You’ll influence technical direction and help define what world-class AI infrastructure operations look like. Andromeda Cluster is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. Please mention the word **FORTUNE** and tag ROTkuMTQ2LjE2LjE0MQ== when applying to show you read the job post completely (#ROTkuMTQ2LjE2LjE0MQ==). This is a beta feature to avoid spam applicants. Companies can search these words to find applicants that read this and see they're human.

Why this listing was indexed

LocalHireHub indexes openings like this — a fully remote software engineering role posted by Andromeda Cluster for the Dunbar, West Virginia market — because they're exactly the kind of listing job seekers in West Virginia are typically searching for: a real, currently open position with a clear schedule and a named employer, not a generic "we're always hiring" page.

What this role typically involves

Roles in Software Engineering at organizations like Andromeda Cluster usually combine day-to-day delivery work with cross-functional coordination. Expect a mix of focused individual work, scheduled meetings with collaborators, written status updates, and the occasional escalation that requires faster turnaround. The exact responsibilities will be detailed in the official posting linked below, but the shape of the work tends to be consistent across employers in this category.

Who this role tends to fit

This role typically suits candidates who already have working experience in Software Engineering and who are comfortable with the fully remote schedule listed here. Strong written communication tends to matter more than people expect, especially in remote and hybrid setups where async updates replace some of the in-person conversation. Familiarity with the tools commonly used in Software Engineering — ticketing systems, collaboration suites, and the relevant industry-specific software — is generally expected.

Working from Dunbar, WV

Dunbar is one of the larger labor markets in West Virginia, which means employers in this region tend to draw from a broad commuter radius. If the schedule is hybrid, the in-office days will be at the employer's Dunbar location; if it's fully remote, your physical address can be anywhere the employer is registered to hire (often, but not always, all 50 states). The detail in the source listing will confirm which states are eligible.

How to apply

Applications are handled directly by Andromeda Cluster. To apply, visit the original listing at Andromeda Cluster's posting. LocalHireHub does not collect resumes or contact information — we link directly to each employer.

Listing source: RemoteOK.