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 looking for a GPU Cluster Engineer to own the entire software stack of our GPU clusters — from kernel tuning and GPU drivers up through schedulers, containers, and ML frameworks. While our Hardware Operations team keeps the physical machines healthy and connected, you define what a production-ready node looks like in software: you author the images, playbooks, and pipelines that take a freshly provisioned server to a fully validated GPU node, and you keep the fleet consistent, upgradable, and fast. 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.

Key Responsibilities

OS Bring-Up & Node Lifecycle Engineering

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.

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.

Self-Healing Operations: Automate detection of unhealthy nodes (Xid/ECC errors, link flaps, thermal throttling), with cordon/drain/reboot/re-image workflows and clean handoff to Hardware Operations for physical repair or RMA.

Configuration Management & Automation

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.

Operational Tooling: Develop Python/Bash tooling for cluster operations, health reporting, and workflow automation.

Orchestration & Scheduling (Kubernetes & Slurm)

Kubernetes for Serving: Deploy and operate GPU-enabled Kubernetes for inference workloads — NVIDIA GPU Operator, device plugins, node feature discovery, topology-aware scheduling, and MIG/MPS partitioning where appropriate.

Training Schedulers: Operate Slurm (or Run:AI) for multi-node training — partitions, QoS, preemption, accounting, and container integration (enroot/pyxis).

Container Platform: Maintain base images, registries, and the NVIDIA Container Toolkit / ROCm container stack; keep training and serving images lean, current, and reproducible.

GPU Driver & ML Stack Engineering

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.

Distributed Performance: Tune NCCL/RCCL across NVLink/NVSwitch and InfiniBand/RoCE fabrics, ensure topology-aware job placement, and run continuous communication/throughput benchmarks to catch regressions.

Advanced Debugging & Observability

Escalation Point: Own the hard problems — NCCL hangs and timeouts, CUDA memory leaks, ROCm kernel crashes, straggler nodes, and unexplained throughput drops.