Staff Network Engineer (AI Fabric, Datacenter and Edge Networking) - Radian Arc

Location & work modality: Remote

Type of Contract: Full time or Contract

About Radian Arc

We’re specialists in outcome-optimized AI infrastructure - deploying, orchestrating and monetizing GPU compute where data, users and demand actually meet: inside telco networks, at the edge, and in core data centers.Not a generic AI platform. Not a consultancy. We’re the bridge between raw silicon and real-world results.

What impact you will have

Design, implement, and operate the network infrastructure powering the GPU cloud platform, including high-performance AI fabrics as well as classical datacenter networking components such as routing, security, and external connectivity. This role spans both high-performance east-west networking for distributed AI workloads and north-south connectivity, security, and inter-datacenter transport.

As the first dedicated networking role in the organization, the Staff Network Engineer combines Staff-level architectural ownership, technical direction, and cross-functional influence with hands-on execution across design, deployment, troubleshooting, automation, and operational improvement.

The Staff Network Engineer owns the long-term technical direction and operational strategy for Radian Arc ’s AI interconnect networks, designing scalable GPU fabrics and ensuring predictable low-latency performance across distributed training and inference workloads. The role includes designing large-scale RoCE and Ethernet fabrics, guiding architecture decisions, and ensuring operational excellence across global deployments, from hyperscale datacenters to smaller edge locations.

You will collaborate closely with platform, compute, storage, observability, and operations teams to ensure networking is deeply integrated into the overall infrastructure architecture. This role also acts as the senior escalation point for complex networking incidents, driving deep technical investigations and systemic improvements that increase reliability, latency consistency, and operational maturity across the platform.

Because this is currently the primary networking role in the company, the position is intentionally hybrid: you are expected to operate at L6 / Staff in terms of technical direction, standards, cross-team influence, and long-term design, while also directly executing critical networking work that, in a larger organization, would be distributed across multiple engineers.

We expect to hire more than one person for this role so providing you have experience in designing AI Infrastructure networking solutions at scale please do not hesitate to apply if your experience does not match the full scope of the position.

What you’ll do

AI Fabric & HPC Networking

• Design and operate high-performance GPU networking fabrics supporting distributed AI workloads
• Architect large-scale RoCE fabrics optimized for distributed training and inference
• Optimize network performance for GPU communication patterns and east-west traffic
• Design fabric topologies such as:

○ Leaf-spine

○ Fat-Tree

○ Rail architectures

○ Multi-plane

• Implement high-performance networking technologies including:

○ RDMA

○ RoCE

○ High-bandwidth east-west fabrics

○ Spectrum-X

• Collaborate with compute teams to support distributed training frameworks and GPU communication libraries
• Define reference architectures and design principles for AI fabrics so future deployments follow reusable standards rather than one-off implementations
• Evaluate architectural trade-offs across performance, resilience, cost, operability, and deployment speed, and make clear recommendations to stakeholders

Datacenter Networking

• Design and operate Layer-2 and Layer-3 datacenter networks
• Implement scalable routing architectures based on BGP and ECMP
• Design tenant network isolation mechanisms across multi-tenant environments
• Implement and maintain:

○ Network bridges

○ Routing stacks

○ Overlay networking systems

• Maintain north-south ingress/egress routing and traffic management
• Define standards and reusable patterns for segmentation, routing, and overlay integration across platform deployments

Technologies include

• VyOS routers
• Linux networking stacks
• OVS / OVN
• BGP / ECMP
• VLAN / VRF segmentation

Security & Edge Connectivity

• Deploy and maintain north-south security infrastructure
• Implement WAF and application-layer protections
• Integrate security controls with platform services

Technologies include

• Citrix NetScaler / Citrix WAF
• TLS termination
• DDoS mitigation
• API and proxy gateway protection

Inter-Datacenter Networking

• Design and operate private interconnects between datacenters
• Implement and maintain dark fiber ring architectures
• Operate high-capacity WAN connectivity between regions
• Integrate datacenter fabrics into a global backbone network
• Define scalable design principles for backbone evolution, inter-site routing, redundancy, and failure-domain isolation

Technologies include

• DWDM / dark fiber transport
• BGP inter-site routing
• Redundant fiber ring architectures
• 100–400G optical transport
• Spectrum-XGS

Engineering Execution & Delivery

• Lead end-to-end engineering delivery of networking infrastructure, from design and labvalidation to production deployment
• Validate network BOMs together with procurement and deployment teams
• Provide detailed input into datacenter layouts and rack elevations
• Drive capacity planning, performance modeling, and scaling strategies
• Ensure network changes are executed safely with minimal customer impact
• Act as both the architectural owner and the practical execution lead for critical network initiatives during the build-out phase of the networking function
• Establish deployment standards, validation criteria, rollback approaches, and acceptance patterns that future engineers and teams can reuse

Operational Excellence & Reliability

• Own operational performance and reliability of networking infrastructure
• Drive automation for:

○ Provisioning

○ Configuration management

○ Monitoring

○ Lifecycle management

• Improve day-2 operations through automation and operational tooling
• Lead incident response and root-cause analysis for major network events
• Define and track SLAs, SLOs, and reliability metrics
• Translate major incidents and operational pain points into durable standards, design changes, and long-term architectural improvements
• Establish measurable benchmarks for reliability, latency consistency, operability, and recovery behavior across network deployments

Cross-Functional Collaboration

• Work closely with infrastructure, platform, SRE, compute, storage, observability, and datacenter operations teams
• Provide technical leadership across infrastructure initiatives
• Communicate architectural decisions, trade-offs, and risks clearly to stakeholders
• Influence the long-term platform networking roadmap and architecture
• Act as the primary networking design authority across the organization, guiding adjacent teams on how networking constraints and capabilities should shape platform decisions
• Raise the technical bar by mentoring engineers in adjacent domains and helping build the future networking function

Technical Stack

Datacenter Networking

• BGP
• EVPN / VXLAN
• ECMP
• VLAN / VRF
• OVS / OVN
• Linux networking
• BlueField DPU

Routing & Control Plane

• VyOS
• BGP-based routing architectures
• ECMP fabrics

Security

• Citrix NetScaler / WAF
• DDoS protection

Transport & Backbone

• Dark fiber
• Metro fiber rings
• DWDM transport
• 100–1600G optical networking

AI Networking

• RDMA
• RoCE
• GPU fabrics
• Large-scale east-west compute networking
• Congestion control

What you'll need

Core Experience

• Strong hands-on experience designing and operating large-scale datacenter networks
• Expert knowledge of modern networking protocols including:

○ BGP

○ OSPF

○ ECMP

○ EVPN / VXLAN

• Proven experience operating high-speed Ethernet networks in production environments
• Experience operating NVIDIA / Mellanox networking platforms
• Experience owning both architecture and direct implementation in lean or fast-scaling environments is strongly preferred

Advanced AI Fabric Networking Expertise

The candidate should have deep expertise in designing and operating networking fabrics optimized for large-scale GPU clusters and distributed AI workloads.

This includes a strong understanding of GPU communication patterns and the networking requirements of distributed training and inference systems.

Relevant expertise includes

• Deep understanding of NCCL communication patterns and their impact on network topology and performance
• Experience tuning RoCE fabrics for large-scale GPU clusters
• Strong knowledge of RDMA transport behavior and failure modes
• Practical experience implementing and tuning PFC and ECN for congestion management
• Understanding of GPU collective communication patterns such as all-reduce, all-gather, broadcast, reduce-scatter, and their impact on east-west network traffic
• Experience designing rail-optimized GPU networking fabrics for distributed training and inference clusters
• Familiarity with diagnosing performance issues related to:

○ NCCL stalls

○ RDMA congestion

○ Fabric hotspots

○ Packet loss impacting distributed training

• Understanding of how networking performance affects distributed AI frameworks such as PyTorch and TensorFlow

The candidate should also be able to collaborate closely with compute platform teams to ensure that networking infrastructure is optimized for distributed training, distributed inference, andhigh-throughput AI workloads.

Systems & Troubleshooting

• Ability to debug complex cross-layer