# Member of Technical Staff - GPU Infrastructure at Hyperbolic

> United States · Staff · DevOps / SRE / Infra · open. Source: https://alderhq.app/jobs/ashby%3Ahyperbolic%3A0cd2cbf3-bc38-4e02-84c1-ac7a7c285105 (read 2026-11-06).

## Facts (observed)

- Company: [Hyperbolic](https://alderhq.app/companies/hyperbolic)
- Location text: San Francisco, CA
- Where it can be done from (inferred): United States (evidence: "Location: San Francisco, CA")
- Posted: 2026-03-26 (226 days ago)
- First seen by alder: 2026-11-02 · last verified: 2026-11-04
- Reposted: 0 times · edited: 0 times
- Apply on the employer's site: https://jobs.ashbyhq.com/hyperbolic/0cd2cbf3-bc38-4e02-84c1-ac7a7c285105
- Skills listed (inferred): Terraform, Distributed systems

## Pay and sponsorship

- Posted pay: not posted
- Market band for similar posted roles (SF Bay Area, n 122): p25 $230k, median $257k, p75 $300k

## Description (the employer's text)

### Who We Are

Hyperbolic Labs is on a mission to democratize AI by breaking down the barriers to computing power with our Open-Access AI Cloud. By making better use of idle computing resources across the globe, we offer an innovative GPU marketplace and AI inference service that promise affordability and accessibility for all. As pioneers at the intersection of AI and open-source technology, we believe in an open future where AI innovation is limited only by imagination, not by access to resources. We're looking for forward-thinking individuals who share our passion for making AI universally accessible, secure, and affordable. Join us in building a platform that empowers innovators everywhere to turn their visionary AI projects into reality.

### About the Role

We're seeking a very senior Infrastructure Engineer to help build and scale Hyperbolic's GPU Cloud Marketplace, by building a multi-tenancy provisioning and virtualization solution. This is a foundational role where you'll be responsible for transforming raw GPUs from diverse global suppliers into a programmable, orchestrated pool that serves thousands of AI developers and researchers. You'll work at the cutting edge of cloud infrastructure, building the core orchestration layer that enables our platform to deliver up to 75% cost savings compared to traditional cloud providers.

### Who You Are

Deep understanding of bare-metal provisioning and lifecycle management, including IPMI/Redfish, BMC-based remote management, PXE boot, and automated OS deployment workflows

Deep understanding of GPU scheduling and orchestration, including GPU type awareness, memory management, topology considerations, placement strategies for multi-GPU jobs, and fragmentation minimization

Strong infrastructure and DevOps engineering skills with proficiency in Terraform or Pulumi, CI/CD for infrastructure, secrets management, configuration management, and observability stack implementation

Experience with storage and data infrastructure for AI/ML workloads, including object storage, high-IOPS block storage, and distributed file systems for training data and checkpoints

Proficiency with API design and cloud-init for automated provisioning and configuration

Solid understanding of GPU architecture, CUDA, and GPU compute optimization

Highly collaborative team player with excellent communication skills across technical and non-technical stakeholders

Proven ability to work effectively with hardware vendors and vendor engineering teams to troubleshoot issues and optimize integrations

Experience building and scaling cloud infrastructure or distributed systems in production environments

### Preferred Qualifications

Familiarity with high-performance networking technologies such as InfiniBand and RoCE (RDMA over Converged Ethernet)

Experience with distributed storage systems such as Ceph, Weka, or VAST Data

Hyperbolic is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

---
Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Ahyperbolic%3A0cd2cbf3-bc38-4e02-84c1-ac7a7c285105 and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.