{"id":1938308,"url":"https://alion.io/job/sciforium-senior-hpc-gpu-infrastructure-engineer","title":"Senior HPC & GPU Infrastructure Engineer","company":{"id":716759,"name":"Sciforium","domain":"sciforium.ai","url":"https://alion.io/company/sciforium-2","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":85,"open_postings":5,"ghost_share":0,"stale_share":0.8,"repost_share":0,"time_to_fill_p50_days":11,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":220000,"currency":"USD","period":"year","gross":null,"usd_annual":220000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Bash","optional":false},{"name":"CentOS Stream","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"cuDNN","optional":false},{"name":"HPC","optional":false},{"name":"JAX","optional":false},{"name":"LDAP","optional":false},{"name":"Linux","optional":false},{"name":"Machine Learning","optional":false},{"name":"Multimodal AI","optional":false},{"name":"NCCL","optional":false},{"name":"NVLink","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"ROCm","optional":false},{"name":"Ubuntu","optional":false},{"name":"vLLM","optional":false},{"name":"Ansible","optional":true},{"name":"Configuration Management","optional":true},{"name":"Kubernetes","optional":true},{"name":"SaltStack","optional":true},{"name":"SLURM","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-06T00:01:03Z","employer_posted_date":"2026-10-06","last_verified_at":"2026-10-13T10:58:11Z","board_verified":true,"closed_at":null,"days_open":7,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":7},"description":"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.\nAbout the role\nWe 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.\nWhat you'll do\n1. System Health & Reliability (SRE)\nOn-Call Response: Act as the primary responder for system outages, GPU failures, node crashes, and cluster-wide incidents. Minimize downtime by resolving issues rapidly.\n\nCluster Monitoring: Implement and maintain monitoring for GPU health, thermal behavior, PCIe/NVLink topology issues, memory errors, and overall system load.\n\nVendor Liaison: Coordinate with data center staff, hardware vendors, and on-site technicians for repairs, RMA processing, and physical maintenance of the cluster.\n\n2. Linux & Network Administration\nOS Management: Install, patch, and maintain Linux distributions (Ubuntu / CentOS / RHEL). Ensure consistent configuration, kernel tuning, and automation for large node fleets.\n\nSecurity & Access Controls: Configure VPNs, iptables/firewalls, SSH hardening, and network routing to secure our computer infrastructure.\n\nIdentity & Storage Management: Manage LDAP/FreeIPA/AD for user identity, and administer distributed file systems such as NFS, GPFS, or Lustre.\n\n3. GPU & ML Stack Engineering\nDeployment & Bring-Up: Lead deployment of new GPU nodes, including BIOS configuration, NUMA tuning, GPU topology validation, and cluster integration.\n\nDriver & Kernel Management: Build and optimize kernel modules, maintain GPU drivers and runtime stacks for both NVIDIA (CUDA) and AMD (ROCm).\n\nSoftware Stack Maintenance: Maintain and optimize ML frameworks and libraries PyTorch, JAX, CUDA toolkit, cuDNN, ROCm, NCCL, and supporting runtime systems.\n\nAdvanced Debugging: Troubleshoot complex interactions involving GPUs, compilers, ML frameworks, and distributed training runtimes (e.g., vLLM compilation failures, CUDA memory leaks, ROCm kernel crashes).\n\nIdeal candidate profile\n5+ years of experience in HPC, GPU cluster operations, Linux systems engineering, or similar roles.\n\nBachelor’s or Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related technical field.\n\nStrong expertise with NVIDIA (H100/B200) or AMD (MI325x/MI355x) GPUs, including driver and kernel-level debugging.\n\nDeep understanding of Linux internals, kernel modules, hardware bring-up, and systems performance tuning.\n\nExperience with network security, including VPNs, iptables/firewalld, SSH, and identity management (LDAP/FreeIPA/AD).\n\nProficiency in Bash and Python for scripting, automation, and workflow tooling.\n\nFamiliarity with ML software stacks: CUDA toolkit, cuDNN, NCCL, ROCm, JAX/PyTorch runtime behavior.\n\nDeep debugging experience with NVLink/NVSwitch fabrics and RDMA networking.\n\nNice-to-have\nExperience with job schedulers such as Slurm, Kubernetes, or Run:AI.\n\nExposure to vLLM, model serving optimizations, or inference systems.\n\nHands-on experience with configuration management tools (Ansible, SaltStack, Terraform).\n\nPrevious experience supporting ML research teams in a startup or research-heavy environment.\n\nBenefits include\nMedical, dental, and vision insurance\n\n401k plan\n\nDaily lunch, snacks, and beverages\n\nFlexible time off\n\nCompetitive salary and equity\n\nEqual opportunity\nSciforium is an equal opportunity employer. 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