Network Systems Architect
Cerebras · Sunnyvale, CA
CHECKED 7H AGO
Infrastructureremoteprincipal
At a glance
Highlights
- Define scale-up network architecture for Cerebras wafer-scale AI platforms.
- Set multi-generation roadmap for proprietary accelerator interconnects.
- Help deliver industry-leading training and inference speeds beyond GPUs.
Ideal candidate
Principal-level architect with 12+ years owning complex networking or HPC systems end-to-end. You thrive turning ambiguous workload behavior into clear fabric trade-offs and staying engaged through bring-up and qualification.
Requirements
- BS, MS, or PhD in Electrical Engineering, Computer Engineering, Computer Science or related field
- Typically 12+ years relevant experience including principal-level ownership from architecture to implementation
- Deep expertise in low-latency interconnects, fabric protocols and switch architecture
- Experience deriving network requirements from incomplete workload and system information
- Working knowledge of Ethernet, IP and RDMA plus familiarity with BGP and EVPN
- Architectural judgment grounded in prior implementation, modeling, bring-up or debugging
Lab profile
About Cerebras
Builds wafer-scale AI chips and cloud systems for ultra-fast training and inference.
What they build
- Wafer-scale AI chips
- AI training systems
- Cloud inference service
Known for
- 56x larger chips than GPUs
- 10x faster inference than GPU clouds
- Industry-leading training speed