Sciforium

Sciforium

GPU Cluster Engineer, Systems & Platform

San Francisco, CA · Senior

Sponsorship not specifiedDetected 78 days ago
PythonBashNode.jsFull-Stack DevelopmentGitDockerKubernetesTerraformAnsibleLinuxPrometheusGrafanaMachine LearningPyTorchLLMsAccountingElectrical EngineeringResearchCommunication

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Sponsors this role80
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PERM / green-card track0
Lottery odds40
Fits your clock70

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About the role

  • You will serve two demanding customer groups - our foundation model training teams and our model serving/product teams - ensuring both run on correctly configured, well-managed, high-performance infrastructure.
  • OS Bring-Up & Node Lifecycle Engineering
  • Orchestration & Scheduling (Kubernetes & Slurm)

Responsibilities

  • Golden Images & Automated Bring-Up: Own the node software definition - versioned OS images, kernel tuning (NUMA, hugepages, IRQ affinity, cgroups), GPU/NIC driver stacks - and the automated pipeline that takes a node from base OS to production-ready.
  • Validation & Burn-In: Build automated acceptance suites (DCGM diagnostics, nccl-tests/RCCL tests, bandwidth and topology checks, HPL) that gate every node before it enters a scheduler pool.
  • enforce configuration consistency, detect drift, and maintain the driver ↔ CUDA/ROCm ↔ framework compatibility matrix across the fleet.
  • Infrastructure as Code: Manage all node and cluster configuration through Ansible/SaltStack playbooks in Git, with peer-reviewed changes, CI validation, and canary rollouts before fleet-wide deployment.
  • Provisioning Pipelines: Build and maintain image/provisioning tooling (PXE, MaaS, Packer, or similar) so new or re-imaged nodes are reproducible, not hand-crafted.
  • Container Platform: Maintain base images, registries, and the NVIDIA Container Toolkit / ROCm container stack
  • Fleet Maintenance: Execute rolling kernel/driver/toolkit upgrades with minimal disruption to running workloads; enforce configuration consistency, detect drift, and maintain the driver ↔ CUDA/ROCm ↔ framework compatibility matrix across the fleet.
  • Container Platform: Maintain base images, registries, and the NVIDIA Container Toolkit / ROCm container stack; keep training and serving images lean, current, and reproducible.
  • Driver & Runtime Lifecycle: Build, deploy, and debug the full accelerator stack - NVIDIA (CUDA toolkit, cuDNN, NCCL, Fabric Manager) and AMD (ROCm, RCCL) - including kernel modules (DKMS), GPUDirect RDMA/Storage, and the RDMA software stack (MOFED/DOCA).
  • Framework Environments: Maintain curated, optimized PyTorch and JAX environments with sane dependency and version management for researchers and production services.

Requirements

  • 5+ years in systems/infrastructure engineering with significant GPU cluster, HPC, or large-scale ML infrastructure experience.
  • Bachelor's or Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.
  • Deep Linux internals expertise: kernel modules/DKMS, systemd, cgroups, NUMA, and system performance tuning.
  • Hands-on experience with NVIDIA (CUDA) and/or AMD (ROCm) driver and runtime stacks on modern accelerators (H200/B200, MI325x/MI355x class), including kernel-level debugging.
  • Client-side experience with distributed filesystems (Lustre, GPFS, Weka) and checkpoint I/O optimization.
  • Proficiency in Python and Bash for automation and tooling.
  • Working knowledge of NCCL and RDMA networking (InfiniBand/RoCE, GPUDirect) and of PyTorch/JAX runtime behavior.
  • Experience deploying and tuning inference/serving stacks (vLLM, Triton Inference Server, TensorRT-LLM) for latency and throughput targets.
  • Must-Haves:
  • Strong configuration management experience (Ansible or SaltStack) with Git-based, code-reviewed infrastructure workflows.
  • Provisioning and image tooling experience (Packer, MaaS, Foreman, Terraform, or similar) for automated, reproducible node builds.
  • Container fluency: Docker/containerd and the NVIDIA Container Toolkit or ROCm equivalent.
  • Nice-to-Haves:

Nice to have

  • Production Kubernetes experience with GPU workloads, plus working knowledge of HPC schedulers (Slurm/Run:AI) - or the reverse (deep Slurm, working K8s).
  • Experience directly supporting foundation model training teams - multi-node job failure debugging, checkpoint pipeline tuning, and framework-level performance triage - ideally in a startup or research-heavy environment.

Compensation

  • Competitive salary and equity

Benefits

  • Medical, dental, and vision insurance
  • Flexible time off
  • Competitive salary and equity
  • Operational Tooling: Develop Python/Bash tooling for cluster operations, health reporting, and workflow automation.

Company info

  • Sciforium is an AI infrastructure company developing next-generation multimodal AI models and a proprietary, high-efficiency serving platform.

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.

Visa & Work Authorization

  • 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.
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This listing is sourced directly from Sciforium's careers page and normalized into a canonical job model.