GPU Cluster Engineer - Hardware Operations
About the job
- At least 3 years of experience in Linux systems administration, including deep knowledge of boot processes, systemd, and disk management.
- A strong background in server hardware troubleshooting in high-density environments, including power, cooling, and PCIe topologies.
- Experience managing networking security, including VPNs, iptables or firewalld, and VLANs, as well as directory services such as LDAP, FreeIPA, or Active Directory.
- Proficiency in Bash scripting for system automation.
- Serve as the primary contact for physical system outages, hardware failures, and network interruptions to minimize downtime.
- Monitor hardware health, including GPU thermals, power draw, and physical system loads, and identify anomalies before they affect active workloads.
- Coordinate RMA processes, physical repairs, part replacements, and routine maintenance with data center facility staff and third-party hardware vendors.
- Rack, cable, and lead physical bring-up of new GPU nodes, ensuring power and network connectivity are integrated with the existing cluster.
- Install, patch, and maintain Linux operating systems, including Ubuntu, CentOS, and RHEL, across bare-metal cluster servers.
- Configure and maintain edge and internal networking, including firewalls and VPNs, and enforce SSH access controls to secure infrastructure.
- Administer LDAP or Active Directory for centralized user authentication and ensure network storage systems, including NFS, GPFS, and Lustre, are reliably mounted and properly permissioned.
- Experience using configuration management tools such as Ansible, SaltStack, or Terraform for OS provisioning.
- Familiarity with data center operations and cooling requirements for high-TDP accelerators such as NVIDIA B200 or AMD MI355x.
About the company
Sciforium provides an AI infrastructure stack with a serverless inference platform, giving access to a mix of open-source and proprietary models through a single API. It runs on its own custom-optimized AMD hardware, delivering models without shared cloud servers to reduce costs, boost performance, and improve data privacy. The company differentiates itself by owning the full stack—hardware, software, and models—and by pursuing byte-native multimodal foundation models. Its goal is to simplify and speed up production-ready AI deployment while lowering costs and complexity for large-scale models.
Company Size
11-50
Company Stage
N/A
Total Funding
N/A
Headquarters
San Francisco, California
Founded
2024
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Simplify's Take
What believers are saying
- SignalFire still lists Sciforium current as of June 2026.
- LinkedIn shows aggressive hiring for GPUs, ML, and growth in August 2026.
- First tranche of GPU capacity targets October 2026, suggesting near-term productization.
What critics are saying
- October 2026 capacity depends on converting one industrial facility on schedule.
- Twelve jobs across GPU, data center, and model-serving roles signal heavy burn.
- No public customers or revenue announcements expose Sciforium to existential platform credibility risk.
What makes Sciforium unique
- AMD-backed vertical stack ties hardware, serving, and models into one platform.
- Byte-native multimodal foundation models and inference platform share the same infrastructure.
- Custom AMD GPU operations target lower TCO than shared-cloud inference competitors.
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Benefits
- Health Insurance
- Dental Insurance
- Vision Insurance
- 401(k) Retirement Plan
- Meal Benefits
- Company Equity