D Matrix
Data Center & Lab Technician - AI Accelerator Infrastructure - Contract
Santa Clara. Posted 4 months ago
The role
You will rack, stack, cable, and decommission hardware in on-premises labs and colocation facilities. You will also perform hardware bring-up, component replacement, and maintain asset records for AI-accelerated systems.
What you will do
- Rack, stack, cable, and decommission servers, PDUs, network gear, and storage
- Execute hardware bring-up for AI-accelerated systems and validation test benches
- Replace and upgrade components including PCIe cards, NICs, storage, memory, and accelerator hardware
- Maintain accurate asset and inventory records in DCIM tools
- Coordinate equipment moves and manage shipping, receiving, and RMA workflows
- Diagnose and resolve hardware, power, thermal, and network connectivity issues
- Write and maintain SOPs, rack diagrams, cabling guides, and runbooks
- Keep lab spaces organized, ESD-compliant, and audit-ready
Required qualifications
- 3+ years in data center operations, hardware validation, or systems technician roles
- Hands-on server experience
- Rack-and-stack experience including server installation, PDU and patch panel cabling, rack power planning, and professional-grade cable management
- Hardware bring-up experience including BIOS/UEFI configuration, firmware updates, component replacement, and OS installation on Linux
- Linux command-line proficiency
- Experience with DCIM or inventory tools
- Strong written communication for documenting work and writing SOPs
- Ability to work independently in a fast-moving startup environment
Nice to have
- Colocation data center experience (Equinix, CoreSite, Digital Realty, or similar)
- Experience with AI/ML or GPU hardware (NVIDIA, AMD, or Intel accelerator cards)
- Familiarity with high-speed interconnects such as InfiniBand, RoCE, and SFP/QSFP optics
- Basic Ansible exposure
- Python or Bash scripting for operational task automation
About the company
d-Matrix designs purpose-built AI inference silicon using a memory-centric architecture. The team tackles AI infrastructure challenges through chips, software, and rack-scale systems.