Member of Technical Staff - Datacenter Networking

Prime Intellect, Inc.; San Francisco (CA); On-site; USD 150,000 - 300,000; Full time

Job summary

Prime Intellect, Inc. is building open frontier AI infrastructure. We are seeking an experienced Network Engineer to design and operate datacenter networks that power large GPU clusters and frontier models.

You will implement scalable leaf-spine topologies, manage Ethernet, InfiniBand/RoCE, and high-performance fabrics; automate with Python and Ansible, and partner with operators and vendors to ensure high availability and performance.

Qualifications

• 3+ years of production datacenter networking experience.
• Strong understanding of Ethernet, TCP/IP, routing, switching, and redundant network design.
• Hands-on experience with high-performance GPU networking using InfiniBand or RoCE.
• Experience troubleshooting network problems across Linux hosts, NICs, switches, and physical links.
• Ability to automate network operations with Python, Ansible, or comparable tools.
• Leaf-spine architectures, BGP, ECMP, VLANs, and network segmentation.

Responsibilities

• Design and operate the networks that connect large GPU clusters.
• Own reliability and performance of training fabrics, storage networks, and management connectivity.
• Scale distributed workloads without bottlenecks by optimizing network paths.
• Collaborate with datacenter operators and hardware vendors on deployment readiness and failure resolution.

Skills

Datacenter networking

Ethernet/TCPIP networks

InfiniBand/RoCE

Network automation

Linux networking

Performance tuning

Tools

Ansible

Python

Job description

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Role Impact

You'll design and operate the networks that connect large GPU clusters. Own the reliability and performance of training fabrics, storage networks, and management connectivity so distributed workloads can scale without the network becoming the bottleneck.

Core Technical Responsibilities

Design and deploy scalable datacenter network topologies for GPU training, inference, storage, and management traffic

Configure and operate high-performance Ethernet/RoCE and InfiniBand fabrics with clear standards for routing, redundancy, and capacity

Automate network provisioning, configuration validation, upgrades, and rollback procedures

Diagnose packet loss, congestion, link failures, and collective communication performance across hosts and switches

Benchmark end-to-end network performance with infrastructure and ML teams, translating workload needs into measurable acceptance criteria

Build monitoring for port health, errors, utilization, congestion, and fabric topology; improve incident response and runbooks

Partner with datacenter operators and hardware vendors on cabling, optics, deployment readiness, and failure resolution

Technical Requirements

Required Experience

3+ years of production datacenter networking experience

Strong understanding of Ethernet, TCP/IP, routing, switching, and redundant network design

Hands-on experience with high-performance GPU networking using InfiniBand or RoCE

Experience troubleshooting network problems across Linux hosts, NICs, switches, and physical links

Ability to automate network operations with Python, Ansible, or comparable tools

Infrastructure Skills

Leaf-spine architectures, BGP, ECMP, VLANs, and network segmentation

RDMA concepts and performance tuning; congestion control and lossless Ethernet considerations

Linux networking, NIC drivers and firmware, packet capture, and throughput/latency testing

Optics, transceivers, cable management, and link-level diagnostics

Safe change management, configuration versioning, telemetry, and alerting

Nice to Have

Experience operating 400G/800G networks or large multi-rack GPU clusters

NVIDIA Spectrum or Quantum networking experience

NCCL performance analysis and distributed training troubleshooting

EVPN/VXLAN, SONiC, or network source-of-truth systems

Experience with network simulation, automated validation, and capacity planning

You'll work directly with customers pushing the boundaries of AI, from startups training foundation models to enterprises deploying massive inference infrastructure. You'll collaborate with our world-class engineering team while having direct impact on systems powering the next generation of AI breakthroughs.

We value expertise and customer obsession - if you're passionate about building reliable, high-performance GPU infrastructure and have a track record of successful large-scale deployments, we want to talk to you.

Compensation

Cash compensation range of $150,000–$300,000 plus equity incentives.