Job description

CUSTOMER RELIABILITY ENGINEER LOCATION: REMOTE/SF-HYBRID · FULL-TIME ABOUT ANDROMEDA Andromeda is a market and infrastructure platform to buy, sell, and operate compute. We believe demand for compute will grow exponentially. So fast that a handful of vertically integrated providers won't be able to scale across operations, capital, supply chains, and politics to serve it. The result is a massive wave of fragmentation, with AI factories of every shape and size coming to market to fill this demand. Our job is to enable all of that fragmented compute to flow through one platform, delivering reliable capacity to model builders, research labs, and inference providers when they need it. We believe every spare electron should be made productive for AI and we're building the platform that makes that possible. We sit at the center of three forces: - Companies that need reliable, high-performance compute fast - A fragmented global supply of GPUs across hyperscalers, neoclouds, and independent data centers - Capital, risk, and operational complexity that most teams are not equipped to manage When we succeed, trillions of dollars of compute will flow through Andromeda. Builders get capacity when they need it. Providers get a reliable way to monetize, operate, and finance infrastructure at scale. Capital gets an easy way to deploy, hedge, and underwrite. In five years, Andromeda won't just participate in the AI infrastructure market. We will shape it. The Role Our customers run large AI training and inference workloads on GPU clusters we source from providers worldwide. When a node goes dark or a job dies eight hours into a run, the Customer Reliability Engineer is who they hear from, and who gets it sorted. The job has three parts. You triage incoming issues and debug them at the Linux and Kubernetes layer. You work provider-side to figure out whose fault something actually is and push external providers to fix it. And you build the monitoring and scripts that catch problems before a customer has to tell us. You need to be comfortable in a Linux shell and know how Kubernetes works. You don't need GPU or HPC experience. Most people pick that up here. What You’ll Do Triage and fix customer issues - Own issues start to finish: reproduce, diagnose, fix or escalate, close the loop - Debug at the Linux layer: processes, networking, storage, kernel logs, resource contention, systemd, journald - Dig into Kubernetes problems like pods stuck pending or crash-looping, node conditions, scheduling failures, resource limits - Work GPU failures: driver and device-plugin issues, XID errors, thermal throttling, nodes that need cordoning or draining, jobs failing across multiple nodes - Escalate when you're past your depth, with the evidence already gathered Handle incidents - Take part in a 24/7 on-call rotation - First response on alerts and customer-reported outages: assess impact, set severity, pull in the right people - Keep customers updated during incidents. Clear status, honest unknowns, no silence - Write up what happened, then turn it into a runbook, an alert, or a fix so it costs less next time Push providers to resolution - Work out whether a fault is provider-side, ours, or the customer's before it gets handed anywhere - Open tickets with compute providers and chase them down rather than waiting - Track recurring provider failures and flag the patterns to the people making sourcing decisions Build the tooling - Write Python or Bash to automate the checks you'd otherwise run by hand - Build and improve monitoring: cluster and node health checks, GPU telemetry, dashboards, alerts that fire on real problems - Keep runbooks and customer docs current as you go What We’re Looking For - Real Linux troubleshooting ability from the command line. You can work a problem through logs, processes, networking, and disk without a script to follow - Working knowledge of Kubernetes: pods, nodes, deployments, services, scheduling, and how to investigate when one of those breaks - Can write a script in Python or Bash to automate something repetitive - Strong writing. You can explain a technical problem to a frustrated customer clearly and without condescension - Good judgment under pressure. You know what to check first, when to escalate, and how to keep people informed while you're still working it out - Willing to join a 24/7 on-call rotation Strong Candidates May Have - Hands-on time with NVIDIA GPUs in production: drivers, CUDA, DCGM, the Kubernetes device plugin - Experience with high-performance networking (InfiniBand, RoCE) or NCCL - Experience with HPC or batch schedulers like Slurm - A previous customer-facing technical role: support engineering, TAM, solutions, professional services - Knowledge of Prometheus, Grafana, Datadog, or similar - IAC: Terraform, Ansible, or Helm - Genuine interest in AI infrastructure and how big training jobs behave Why You’ll Love It Here - High-growth environment: Get in early at a company at the center of the AI infrastructure boom - Competitive compensation: + meaningful equity - Comprehensive benefits: for you and your dependents, including healthcare, dental, and vision coverage, 401(k), and unlimited PTO 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.

Requirements

Must-haves

You need to be comfortable in a Linux shell and know how Kubernetes works. You don't need GPU or HPC experience. Most people pick that up here.
What You’ll Do
Triage and fix customer issues
Own issues start to finish: reproduce, diagnose, fix or escalate, close the loop
Debug at the Linux layer: processes, networking, storage, kernel logs, resource contention, systemd, journald
Dig into Kubernetes problems like pods stuck pending or crash-looping, node conditions, scheduling failures, resource limits
Work GPU failures: driver and device-plugin issues, XID errors, thermal throttling, nodes that need cordoning or draining, jobs failing across multiple nodes
Escalate when you're past your depth, with the evidence already gathered
Handle incidents
Take part in a 24/7 on-call rotation
First response on alerts and customer-reported outages: assess impact, set severity, pull in the right people
Keep customers updated during incidents. Clear status, honest unknowns, no silence
Write up what happened, then turn it into a runbook, an alert, or a fix so it costs less next time
Push providers to resolution
Work out whether a fault is provider-side, ours, or the customer's before it gets handed anywhere
Open tickets with compute providers and chase them down rather than waiting
Track recurring provider failures and flag the patterns to the people making sourcing decisions
Build the tooling
Write Python or Bash to automate the checks you'd otherwise run by hand
Build and improve monitoring: cluster and node health checks, GPU telemetry, dashboards, alerts that fire on real problems
Keep runbooks and customer docs current as you go
What We’re Looking For
Real Linux troubleshooting ability from the command line. You can work a problem through logs, processes, networking, and disk without a script to follow
Working knowledge of Kubernetes: pods, nodes, deployments, services, scheduling, and how to investigate when one of those breaks

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aikubernetespythongo

Platform: ashby
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401(K)

Pto