Senior HPC & GPU Infrastructure Engineer

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 with hands-on support from AMD engineers the team is scaling rapidly to build the full stack powering frontier AI models and real-time applications.

About the role

We are seeking a Senior HPC & GPU Infrastructure Engineer to take full ownership of the health, reliability, and performance of our GPU compute cluster. You will be the primary custodian of our high-density accelerator environment and the linchpin between hardware operations, distributed systems, and machine learning workflows. This role spans everything from hands-on Linux systems engineering and GPU driver bring-up to maintaining the ML software stack (CUDA/ROCm, PyTorch, JAX, vLLM).

If you love squeezing every bit of performance out of hardware, enjoy debugging GPUs at scale, and want to build world-class AI infrastructure, this role is for you.

What you'll do

• 1. System Health & Reliability (SRE)
• On-Call Response: Act as the primary responder for system outages, GPU failures, node crashes, and cluster-wide incidents. Minimize downtime by resolving issues rapidly.
• Cluster Monitoring: Implement and maintain monitoring for GPU health, thermal behavior, PCIe/NVLink topology issues, memory errors, and overall system load.
• Vendor Liaison: Coordinate with data center staff, hardware vendors, and on-site technicians for repairs, RMA processing, and physical maintenance of the cluster.
• 2. Linux & Network Administration
• OS Management: Install, patch, and maintain Linux distributions (Ubuntu / CentOS / RHEL). Ensure consistent configuration, kernel tuning, and automation for large node fleets.
• Security & Access Controls: Configure VPNs, iptables/firewalls, SSH hardening, and network routing to secure our computer infrastructure.
• Identity & Storage Management: Manage LDAP/FreeIPA/AD for user identity, and administer distributed file systems such as NFS, GPFS, or Lustre.
• 3. GPU & ML Stack Engineering
• Deployment & Bring-Up: Lead deployment of new GPU nodes, including BIOS configuration, NUMA tuning, GPU topology validation, and cluster integration.
• Driver & Kernel Management: Build and optimize kernel modules, maintain GPU drivers and runtime stacks for both NVIDIA (CUDA) and AMD (ROCm).
• Software Stack Maintenance: Maintain and optimize ML frameworks and libraries PyTorch, JAX, CUDA toolkit, cuDNN, ROCm, NCCL, and supporting runtime systems.
• Advanced Debugging: Troubleshoot complex interactions involving GPUs, compilers, ML frameworks, and distributed training runtimes (e.g., vLLM compilation failures, CUDA memory leaks, ROCm kernel crashes).
• Ideal candidate profile
• 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 a 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.