Neo Cloud | AI Cloud Network Engineering, MOT

We’re looking for an exceptional Member of Technical Staff to design, build and operate Gridfield’s cloud network - from the data center fabric, through the network functions that protect and connect our tenants, out to the ISPs, internet exchanges, other clouds and our own sites. This is a hands-on senior individual-contributor role for a network engineer who configures production gear themselves, automates everything they touch, and owns the network end to end in production., Data Center Network

• Design, deploy, and operate the data center network for our GPU clusters: leaf-spine (Clos) fabrics running BGP, EVPN/VXLAN, and ECMP across the front-end, storage, and out-of-band management networks.
• Build multi-tenant isolation into the fabric with VRFs, VLANs/VNIs, overlays, and policy-based segmentation, so every customer gets a private, high-performance network.
• Bring up new clusters and sites: turn designs into configurations, validate cabling and optics, and run burn-in and acceptance testing with our data center and hardware partners.
• Partner with compute and storage engineers on the RDMA fabrics (RoCE/InfiniBand) that move data between GPUs and storage, including QoS and congestion control., * Design and operate the network functions that sit at the edge of and between tenants: NAT/CGNAT, stateful firewalls and ACLs, load balancers, VPN gateways, DDoS protection, DNS/DHCP, and IPAM.
• Choose between hardware appliance, virtual (VNF), and host/DPU-based implementations, weighing throughput, state, failover, and cost.
• Build security into the network: zero-trust segmentation, management-plane hardening, and audited, reviewable changes., * Build private connections to other clouds (AWS Direct Connect, Google Cloud Interconnect, Azure ExpressRoute, OCI FastConnect) and to customer networks, with IPsec VPN where a dedicated link doesn’t make sense.
• Design and run the backbone between our own sites (wavelengths, dark fiber, MPLS or segment routing), with traffic engineering and capacity planning as we add regions.
• Select and manage carriers, colocation providers, and network hardware vendors: circuits, cross-connects, hardware qualification, and RMAs., * Run the network as code: a source of truth, templated configurations, Git-based review, CI validation, and automated rollout and rollback.
• Work with our AI agents and digital twin to validate changes before they reach production and to let agents deploy and monitor the network safely.
• Instrument the network with streaming telemetry, sFlow/IPFIX, and synthetic probes; build alerting that catches problems before customers do.
• Take end-to-end ownership of the network in production: participate in on-call, lead incident response and root-cause analysis, and drive long-term reliability and performance improvements.
• Lead capacity planning for rapid growth, from port counts and optics to transit and interconnect bandwidth.

Technical Leadership

• Strong bias to action and resolution of technical decisions.
• Set network standards, change-management practices, and runbooks; author and review design documents for significant changes.
• Partner with storage, compute, and security engineers to deliver complete AI infrastructure.
• Mentor engineers and raise the bar through design review, config review, and hands-on collaboration.

Network Architecture/Engineering, Cloud Computing, Artificial Intelligence (AI), Computer Engineering, Intel Product Family, Vehicle Fleets, Cloud Computing, Artificial Intelligence (AI), Internet eXchanges, Network Architecture/Engineering, * 8+ years designing, building, and operating large-scale production networks at a cloud provider, neocloud, hyperscaler, service provider, or HPC site.

• Deep TCP/IP knowledge and hands-on expertise with BGP, IS-IS/OSPF, EVPN/VXLAN, ECMP, and QoS in leaf-spine data center fabrics, including IPv6.
• Hands-on experience configuring and operating network functions in production: NAT/CGNAT, stateful firewalls, load balancers, IPsec VPN, DNS/DHCP, and IPAM.
• Experience connecting networks to the outside world: IP transit and peering, internet exchanges, BGP policy and route security, and private interconnects to AWS, Google Cloud, Azure, or OCI.
• Experience with multi-site networks: backbone and WAN design, MPLS or segment routing, and routing across multiple data centers.
• Production experience with several vendors and network operating systems, such as Arista EOS, Juniper Junos, Cisco NX-OS/IOS-XR, NVIDIA Cumulus, or SONiC.
• Strong Linux skills, including the Linux networking stack, and network automation in Python with Git-based workflows and a configuration management tool (Ansible, Salt, or similar).
• Strong troubleshooting from packet captures to routing tables to vendor bugs, using tools like tcpdump, Wireshark, and mtr.
• Proven experience owning networks in production, including on-call, incident response, and driving systemic reliability improvements.
• Excellent written and verbal communication skills., * Hands-on experience with RoCEv2 and/or InfiniBand fabrics for GPU clusters, and an understanding of AI training and inference traffic patterns.
• Optical transport and DWDM experience, and qualifying high-speed optics (400G/800G).
• Experience running network functions on DPUs/SmartNICs (e.g., NVIDIA BlueField) or software data planes (eBPF/XDP, DPDK, VPP).
• Software-defined networking and cloud overlays; Kubernetes networking and CNIs such as Cilium or Calico.
• Network observability tooling such as gNMI streaming telemetry, Prometheus, Grafana, or OpenTelemetry.
• Experience bringing up the network for a greenfield data center or a new cloud region.
• Experience validating and safely deploying AI-generated automation and configuration.
• Certifications such as CCIE or JNCIE.

Neo Cloud is a fast growing next-generation AI neocloud, founded by ex NVIDIA, CoreWeave and Intel engineering leaders. We offer you the opportunity to build and operate next generation AI infrastructure that powers the world’s most demanding AI workloads for the largest AI labs. We are rethinking how to run large scale AI infra for instance by leveraging agentic AI to build digital twins to derisk our physical deployments. Our agents are deploying and monitoring our fleet to maximize the uptime of our infra.