Senior HPC & GPU Infrastructure Engineer

Sciforium
San Francisco, CA
On-site

Who this role is best for

Candidates with hands-on GPU cluster experience and systems engineering expertise will find this high-performance computing role in San Francisco, CA, ideal for those aiming to build AI infrastructure at scale.

Best fit for

  • Candidates with strong GPU driver and kernel-level debugging skills and a background in HPC or distributed systems.
    — “Strong expertise with NVIDIA (H100/B200) or AMD (MI325x/MI355x) GPUs, including driver and kernel-level debugging.”
  • Individuals with experience in Linux systems engineering and managing large node fleets for high-density environments.
    — “Install, patch, and maintain Linux distributions (Ubuntu / CentOS / RHEL). Ensure consistent configuration, kernel tuning, and automation for large node fleets.”
  • Professionals who have worked with ML software stacks and can optimize frameworks like PyTorch, JAX, and ROCm.
    — “Maintain and optimize ML frameworks and libraries PyTorch, JAX, CUDA toolkit, cuDNN, ROCm, NCCL, and supporting runtime systems.”

Things to consider

  • On-call responsibilities are a core part of the role, requiring rapid response to system outages and GPU failures.
    — “On-Call Response: Act as the primary responder for system outages, GPU failures, node crashes, and cluster-wide incidents.”
  • The role requires direct coordination with hardware vendors and on-site technicians for physical maintenance.
    — “Coordinate with data center staff, hardware vendors, and on-site technicians for repairs, RMA processing, and physical maintenance of the cluster.”

How to stand out

  • Highlight experience with GPU topology validation and cluster integration in your resume and interviews.
    — “Lead deployment of new GPU nodes, including BIOS configuration, NUMA tuning, GPU topology validation, and cluster integration.”
  • Showcase your ability to debug complex GPU interactions, such as memory leaks and kernel crashes.
    — “Troubleshoot complex interactions involving GPUs, compilers, ML frameworks, and distributed training runtimes.”
Pace · SteadyCollaboration · MediumAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

  • deploy new gpu nodes
  • maintain ml software stack
  • troubleshoot complex interactions
Typical background
bachelor’s or master’s degree in computer science, computer engineering, electrical engineering

Skills & requirements

Required

Linux Systems EngineeringGpu Cluster OperationsHpcCudaRocmPyTorchJaxVllmNetwork SecurityLdapNfsGpfsLustre

Preferred

Bash ScriptingPython Scripting

Stack & domain

HPCGPULinux Systems EngineeringGPU Driver Bring-upML Software StackCUDARocmPyTorchJAXVllmLinux DistributionsUbuntuCentosRHELNetwork SecurityVpnsIptablesSSHLDAPFreeipaADNFSGPFSLustreBIOS ConfigurationNUMA TuningGPU Topology ValidationCluster IntegrationKernel ModulesGPU DriversRuntime StacksML FrameworksLibrariesBashPythonProblem-solvingTeamworkCommunicationLeadershipInitiativeAttention To DetailTechnical LiteracyAI InfrastructureMultimodal AI ModelsHigh-efficiency Serving PlatformGPU Compute ClusterHigh-density Accelerator EnvironmentDistributed SystemsMachine Learning WorkflowsHardware Operations

About the role

Original posting from Sciforium via Ashby

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

  • 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.
  • 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.
  • 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.

BENEFITS INCLUDE

  • Medical, dental, and vision insurance
  • 401k plan
  • Daily lunch, snacks, and beverages
  • Flexible time off
  • Competitive salary and equity

EQUAL OPPORTUNITY

Sciforium is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

Source: Sciforium careers (Ashby)

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