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

Jobgether; France; Hybride; EUR 120 000 - 180 000; Plein temps

Avantages offerts par ce poste

Flexible hybrid work

Career growth opportunities

Autonomy over networking architecture

Work on AI infrastructure

International collaboration

Résumé du poste

Jobgether, partnering with a leading AI infrastructure company in France, seeks a Staff Network Engineer to own and evolve high-performance AI networking fabrics across datacenter and edge environments.

You will design, deploy, and operate RoCE and Ethernet fabrics, guide inter-datacenter connectivity, and drive automation and reliability for GPU workloads. This role offers substantial architectural influence and cross-team collaboration.

Qualifications

• Extensive hands-on experience designing, deploying, and operating large-scale datacenter networks.
• Expert knowledge of modern networking protocols and architectures (BGP, OSPF, ECMP, EVPN/VXLAN).
• Proven experience operating high-speed Ethernet networks and networking hardware in production.
• Strong experience with NVIDIA/Mellanox networking platforms and high-performance interconnect technologies.
• Deep expertise designing and operating fabrics for large-scale GPU clusters and distributed AI workloads.
• Hands-on experience with RoCE/RDMA fabrics for GPU clusters.
• Strong automation skills with Python, Bash, or similar.
• Experience owning architecture and driving engineering standards across teams.

Responsabilités

• Own the long-term technical direction, architecture, and operational strategy for high-performance AI networking.
• Design, deploy, and operate GPU networking fabrics for distributed AI workloads.
• Architect large-scale RoCE and Ethernet fabrics and inter-datacenter networks.
• Lead end-to-end delivery of networking initiatives from design to production handover.
• Collaborate with compute, storage, SRE, and datacenter teams in a fast-scaling environment.
• Develop scalable principles for inter-site routing, redundancy, and backbone evolution.
• Drive automation for provisioning, configuration management, monitoring, and lifecycle management.
• Mentor engineers and establish standards and practices for the networking function.

Connaissances

BGP/OSPF

EVPN/VXLAN

RoCE/RDMA

GPU networking

Python scripting

Automation

Observability

Architectural direction

Mentoring

Distributed AI networking

Outils

NVIDIA/Mellanox platforms

Description du poste

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Staff Network Engineer (AI Fabric, Datacenter and Edge Networking) based in France.

This is a Staff-level networking role responsible for designing and operating infrastructure powering large-scale AI and GPU workloads.
You’ll own the architecture and operational strategy for high-performance AI fabrics, datacenter networks, edge connectivity, and inter-datacenter transport.
The role combines deep technical ownership with hands-on execution across design, deployment, automation, troubleshooting, and reliability.
You’ll work on demanding networking challenges involving RoCE, RDMA, high-speed Ethernet, GPU communication, routing, security, and optical transport.
As the primary networking specialist, you’ll have significant influence over architectural standards, deployment patterns, and the evolution of the networking function.
You’ll collaborate closely with compute, platform, storage, SRE, observability, operations, and datacenter teams in a fast-scaling environment.
This is an opportunity to shape networking foundations for distributed AI infrastructure while raising the technical bar across the wider engineering organization.

Accountabilities

• Own the long-term technical direction, architecture, and operational strategy for high-performance AI networking and broader infrastructure networking.
• Design, deploy, and operate GPU networking fabrics optimized for distributed AI training and inference workloads.
• Architect large-scale RoCE and Ethernet fabrics, including leaf-spine, fat-tree, rail, and multi-plane topologies.
• Optimize east-west networking for GPU communication patterns and high-throughput distributed workloads, working closely with compute and platform teams.
• Implement and operate technologies including RDMA, RoCE, high-bandwidth Ethernet, Spectrum-X, and related AI networking platforms.
• Define reusable reference architectures, design principles, deployment standards, validation criteria, and operational patterns for future AI fabric deployments.
• Evaluate architectural trade-offs across performance, resilience, scalability, cost, operability, and deployment speed, providing clear recommendations to stakeholders.
• Design and operate Layer 2 and Layer 3 datacenter networks using technologies such as BGP, ECMP, EVPN/VXLAN, VLAN, VRF, OVS/OVN, and Linux networking.
• Build scalable routing, segmentation, overlay, tenant-isolation, and north-south traffic-management architectures.
• Design and maintain edge security and connectivity infrastructure, including WAF, TLS termination, DDoS mitigation, API gateways, and proxy protections.
• Design and operate private inter-datacenter connectivity, including dark fiber, metro fiber rings, DWDM transport, high-capacity WAN links, and redundant backbone architectures.
• Develop scalable principles for inter-site routing, redundancy, failure-domain isolation, and backbone evolution.
• Lead end-to-end delivery of networking initiatives, from architecture and lab validation through production deployment and operational handover.
• Collaborate with procurement and deployment teams on network bills of materials, datacenter layouts, rack elevations, capacity planning, performance modeling, and scaling strategies.
• Establish safe network-change practices, including deployment validation, rollback procedures, acceptance criteria, and minimal-impact production execution.
• Drive automation for provisioning, configuration management, monitoring, validation, and network lifecycle management using software engineering practices.
• Own network reliability and operational performance, defining and tracking SLAs, SLOs, latency, recovery, and other key reliability metrics.
• Serve as the senior escalation point for complex networking incidents, leading deep investigations, root-cause analysis, remediation, and long-term systemic improvements.
• Build network observability and operational tooling that improves visibility, reliability, and day-two operations.
• Act as the primary networking design authority, influencing platform architecture and helping adjacent teams understand how networking capabilities and constraints affect their decisions.
• Mentor engineers across adjacent domains and help establish the standards, practices, and technical foundations for the future networking organization.

Requirements:

• Extensive hands‑on experience designing, deploying, and operating large-scale datacenter networks in production environments.
• Expert knowledge of modern networking protocols and architectures, including BGP, OSPF, ECMP, and EVPN/VXLAN.
• Proven experience operating high-speed Ethernet networks and networking hardware in production.
• Strong experience with NVIDIA/Mellanox networking platforms and high-performance interconnect technologies.
• Deep expertise designing and operating networking fabrics for large-scale GPU clusters and distributed AI workloads.
• Strong understanding of GPU communication patterns and how distributed training and inference requirements influence network architecture and performance.
• Practical experience with NCCL communication patterns, including all‑reduce, all‑gather, broadcast, and reduce‑scatter, and their impact on network traffic.
• Hands‑on experience designing and tuning RoCE/RDMA fabrics for GPU clusters.
• Strong understanding of RDMA transport behavior, failure modes, congestion, and performance characteristics.
• Practical experience implementing and tuning congestion‑management technologies such as PFC and ECN.
• Experience designing rail‑optimized GPU networking fabrics and diagnosing issues such as NCCL stalls, RDMA congestion, fabric hotspots, and packet loss affecting distributed workloads.
• Understanding of how networking performance affects distributed AI frameworks such as PyTorch and TensorFlow.
• Strong ability to troubleshoot complex cross‑layer issues spanning hardware, firmware, operating‑system networking, kernels, and distributed application communication.
• Strong knowledge of networking hardware, optics, and high-speed interconnects, including dark fiber, DWDM, and high‑capacity optical networking.
• Experience designing network observability solutions and operational tooling.
• Strong automation skills using Python, Bash, or similar technologies, with experience applying software engineering principles to infrastructure automation.
• Ability to build reusable tools, standards, and validation approaches that increase engineering leverage across teams.
• Proven ability to lead complex technical initiatives across engineering, operations, vendors, and other stakeholders.
• Demonstrated ability to establish architectural direction and drive adoption of engineering standards through technical influence rather than formal authority.
• Strong systems‑level thinking, balancing performance, reliability, scalability, operational simplicity, and cost efficiency.
• Strong communication skills, with the ability to clearly explain architectural decisions, risks, trade‑offs, and technical recommendations.
• Strong mentoring skills and willingness to raise the technical capabilities of engineers in adjacent domains.
• Experience owning both architecture and direct implementation in a lean, rapidly scaling organization is strongly preferred.
• Comfortable balancing immediate execution requirements with long‑term architectural sustainability and operational maturity.
• Willingness to travel as required across local, EMEA, US, APAC, or global locations.

Benefits:

• Attractive compensation package reflecting your experience, technical expertise, and impact.
• Flexible and hybrid‑friendly working environment.
• Opportunity to work within an internationally diverse and collaborative team.
• Career growth opportunities within a fast-growing technology scale‑up.
• Significant autonomy and technical ownership over foundational networking architecture.
• Opportunity to work on advanced AI infrastructure, GPU fabrics, high-speed networking, and distributed computing environments.
• Opportunity to shape networking standards, automation, and operational practices from an early stage of the function.
• Inclusive workplace committed to equal opportunity and diverse perspectives.