AI Summary
We are expanding to new frontiers to find the brightest that work in AI infrastructure, research and engineering. Provision, configure, and operate Kubernetes-based clusters for customers across multiple providers. Build automation and tooling to streamline cluster deployments and integrations.
Key Highlights
Provision and operate Kubernetes-based clusters
Build automation and tooling
Debug customer issues
Key Responsibilities
Provision, configure, and operate Kubernetes-based clusters for customers
Build automation and tooling to streamline cluster deployments and integrations
Debug customer issues across networking, storage, scheduling, and system layers
Technical Skills Required
Benefits & Perks
Remote work
Full-time employment
Nice to Have
Exposure to ML/AI infrastructure or GPU-based systems
Familiarity with high-performance networking or distributed storage
Job Description
Location: Global Remote / San Francisco
- Full-Time
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.
What You’ll Do
- Provision, configure, and operate Kubernetes-based clusters for customers across multiple providers.
- Build automation and tooling to streamline cluster deployments and integrations.
- Debug customer issues across networking, storage, scheduling, and system layers.
- Improve reliability and scalability of both training and inference infrastructure.
- Design and implement monitoring, alerting, and observability for critical systems.
- Collaborate with engineering and product teams to plan and deliver infrastructure for new services.
- Participate in on-call and incident response, leading postmortems and reliability improvements.
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- 5+ years experience in SRE, DevOps, or infrastructure engineering roles.
- Strong Linux systems and networking fundamentals.
- Deep experience with Kubernetes and container orchestration at scale.
- Proficiency with Infrastructure-as-Code (Terraform, Helm, Ansible, etc.).
- Strong automation and scripting skills (Python, Go, or Bash).
- Experience with observability stacks (Prometheus, Grafana, Loki, Datadog, etc.).
- Track record of operating production systems and leading incident response.
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- Exposure to ML/AI infrastructure or GPU-based systems (CUDA, Slurm, Triton, etc.).
- Familiarity with high-performance networking (InfiniBand, NVLink) or distributed storage (VAST, Weka, Ceph).
- Customer-facing support or consulting experience.
This is a builder’s role. You’ll have ownership and autonomy to shape how our systems run, working directly with customers and providers while building the foundation for reliable, scalable AI infrastructure.
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