AI Infrastructure Engineer

Sciforium•San Francisco, CA

2d•$150,000 - $220,000

About The Position

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. We are looking for an AI Infrastructure 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.

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.
Production Kubernetes experience with GPU workloads, plus working knowledge of HPC schedulers (Slurm/Run:AI) — or the reverse (deep Slurm, working K8s).
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.
Client-side experience with distributed filesystems (Lustre, GPFS, Weka) and checkpoint I/O optimization.
Container fluency: Docker/containerd and the NVIDIA Container Toolkit or ROCm equivalent.
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.

Nice To Haves

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.
Experience deploying and tuning inference/serving stacks (vLLM, Triton Inference Server, TensorRT-LLM) for latency and throughput targets.
GPU/system profiling tools: Nsight Systems/Compute, rocprof, perf, eBPF.

Responsibilities

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.
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.
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.
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.
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.
Build and maintain image/provisioning tooling (PXE, MaaS, Packer, or similar) so new or re-imaged nodes are reproducible, not hand-crafted.
Develop Python/Bash tooling for cluster operations, health reporting, and workflow automation.
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.
Operate Slurm (or Run:AI) for multi-node training — partitions, QoS, preemption, accounting, and container integration (enroot/pyxis).
Maintain base images, registries, and the NVIDIA Container Toolkit / ROCm container stack; keep training and serving images lean, current, and reproducible.
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).
Maintain curated, optimized PyTorch and JAX environments with sane dependency and version management for researchers and production services.
Tune NCCL/RCCL across NVLink/NVSwitch and InfiniBand/RoCE fabrics, ensure topology-aware job placement, and run continuous communication/throughput benchmarks to catch regressions.
Own the hard problems — NCCL hangs and timeouts, CUDA memory leaks, ROCm kernel crashes, straggler nodes, and unexplained throughput drops.
Own software-layer monitoring (DCGM exporter, Prometheus/Grafana, alerting) plus job-level GPU utilization and cluster efficiency reporting.