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Troubleshoot customer issues across Linux, Kubernetes, GPU clusters, networking, storage, and system services. Participate in 24/7 incident response, communicate with customers, coordinate provider resolution, and create runbooks, monitoring, dashboards, alerts, and automation using Python or Bash. The role also investigates recurring infrastructure failures and improves reliability before incidents affect customers.

Troubleshoot customer AI infrastructure issues across Linux and Kubernetes, diagnose GPU and cluster failures, participate in 24/7 incident response, coordinate provider resolutions, and communicate clearly with customers. Build Python or Bash automation, monitoring, dashboards, alerts, runbooks, and documentation to prevent recurring problems. Experience with NVIDIA GPUs, HPC networking, schedulers, observability tools, and infrastructure-as-code is advantageous but not required.

Own the full sales cycle for AI infrastructure customers, from outbound prospecting and technical validation through negotiation and close. Activate existing relationships with AI founders, infrastructure leaders, and ML teams; build qualified pipeline; maintain accurate forecasts and CRM data; and partner with Solutions Engineering on evaluations. Advise technical buyers on architecture, training and inference economics, GPU supply, and cluster tradeoffs. Travel up to 35% for customer meetings, events, and site visits.

Own technical engagements end-to-end for large customers: lead technical discovery and POCs, design GPU/HPC cluster and workload architectures, provision and validate production training runs, optimize performance and cost, and build SA assets (runbooks, benchmarks, reference architectures). Act as named technical owner to grow accounts and feed product/engineering with customer feedback.

Design and operate large-scale GPU infrastructure for distributed AI training, ensuring reliability, performance, and efficient customer partnerships.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

As a Software Engineer in AI Infrastructure, you will design and develop core platform components, build APIs and services, enhance performance, and automate tooling while collaborating across teams and improving system reliability.

The Head of Partnerships will manage the full partnership lifecycle, develop strategies and agreements with partners, and drive cross-functional coordination to enhance Andromeda's offerings.

As a Compute Trader, you will optimize compute resource utilization, negotiate with suppliers, and develop a proactive strategy informed by market intelligence, focusing on supply and demand dynamics in a fast-paced market.