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Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary high-efficiency serving platform. Backed by multi-million-dollar funding and direct sponsorship from AMD, the company is scaling rapidly to build the full stack powering frontier AI models and real-time applications.
You will take full ownership of the health, reliability, and performance of the GPU compute cluster, serving as the primary custodian of the high-density accelerator environment and the linchpin between hardware operations, distributed systems, and machine learning workflows.
Key responsibilities include:
**System Health & Reliability (SRE):** Act as the primary responder for system outages, GPU failures, node crashes, and cluster-wide incidents, minimizing downtime through rapid resolution. Implement and maintain comprehensive monitoring for GPU health, thermal behavior, PCIe/NVLink topology issues, memory errors, and overall system load. Coordinate with data center staff, hardware vendors, and on-site technicians for repairs, RMA processing, and physical maintenance.
**Linux & Network Administration:** Install, patch, and maintain Linux distributions (Ubuntu/CentOS/RHEL) with consistent configuration, kernel tuning, and automation for large node fleets. Configure VPNs, iptables/firewalls, SSH hardening, and network routing to secure infrastructure. Manage LDAP/FreeIPA/AD for user identity and administer distributed file systems such as NFS, GPFS, or Lustre.
**GPU & ML Stack Engineering:** Lead deployment of new GPU nodes including BIOS configuration, NUMA tuning, GPU topology validation, and cluster integration. Build and optimize kernel modules, maintain GPU drivers and runtime stacks for both NVIDIA (CUDA) and AMD (ROCm). Maintain and optimize ML frameworks including PyTorch, JAX, CUDA toolkit, cuDNN, ROCm, NCCL, and supporting runtime systems. Troubleshoot complex interactions involving GPUs, compilers, ML frameworks, and distributed training runtimes (e.g., vLLM compilation failures, CUDA memory leaks, ROCm kernel crashes).
This role spans everything from hands-on Linux systems engineering and GPU driver bring-up to maintaining the ML software stack. You will be the primary on-call responder and the technical authority for cluster infrastructure.
**Requirements:**
- 5+ years of experience in HPC, GPU cluster operations, Linux systems engineering, or similar roles
- Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or related technical field
- Strong expertise with NVIDIA (H100/B200) or AMD (MI325x/MI355x) GPUs, including driver and kernel-level debugging
- Deep understanding of Linux internals, kernel modules, hardware bring-up, and systems performance tuning
- Experience with network security, including VPNs, iptables/firewalld, SSH, and identity management (LDAP/FreeIPA/AD)
- Proficiency in Bash and Python for scripting, automation, and workflow tooling
- Familiarity with ML software stacks: CUDA toolkit, cuDNN, NCCL, ROCm, JAX/PyTorch runtime behavior
- Deep debugging experience with NVLink/NVSwitch fabrics and RDMA networking
**Nice-to-have:**
- Experience with job schedulers such as Slurm, Kubernetes, or Run:AI
- Exposure to vLLM, model serving optimizations, or inference systems
- Hands-on experience with configuration management tools (Ansible, SaltStack, Terraform)
- Previous experience supporting ML research teams in a startup or research-heavy environment
