We are hiring a Staff Network Engineer to help build and operate the backbone of a carrier-grade, high-performance AI infrastructure. As a key technical contributor, you will design, deploy, and support large-scale network systems that connect our GPU clusters, high-throughput storage, and compute environments across geographically distributed data centers.
You will work alongside Principal Engineers and cross-functional teams to deliver automation-driven, low-latency networking designed for the scale and intensity of AI workloads and HPC environments.
This role is only hiring in APAC.
Key Responsibilities
Implement and maintain high-throughput, low-latency networks supporting AI Factory workloads and distributed training infrastructure.
Work hands-on to deploy, configure, and troubleshoot routing, switching, optics, and interconnect systems across data centers.
Operate and optimize layer 2/3 network services: BGP, EVPN/VXLAN, OSPF, MPLS, QoS, and ACLs.
Work with Infiniband Networking Systems and Nvidia Fabric Manager (UFM)
Develop and maintain network automation (e.g., Ansible, Python, Terraform) for provisioning, compliance, and operational workflows.
Monitor network health and performance using telemetry tools and help scale observability platforms.
Participate in the incident response rotation and perform root cause analysis on service-impacting events.
Maintain configuration standards, documentation, and change management in line with infrastructure governance processes.
Collaborate with the Principal Network Engineer on architectural decisions and vendor evaluations.
Qualifications
Required:
5–8+ years of hands-on experience in large-scale network engineering, data center networks, or service provider infrastructure
Strong knowledge of IP networking, BGP, OSPF, EVPN/VXLAN, and L2/L3 design principles
Experience configuring and operating Arista, Juniper, or Cisco platforms in production environments
Proficiency in scripting or automation (e.g., Python, Bash, Ansible)
Solid troubleshooting skills and experience with real-time diagnostics and packet analysis
Familiarity with monitoring and telemetry tools (e.g., Prometheus, Grafana, sFlow, InfluxDB)
Preferred:
Experience in AI, HPC, or GPU-based infrastructure
Exposure to carrier-grade architectures, DCI, and optical transport systems
Exposure to Nvidia Infiniband Networking systems and components.
Understanding of network segmentation, security policies, and zero-trust principles
Comfortable working in 24/7 operational environments and on-call rotations
Voltage Park is an equal opportunity employer and makes employment decisions on the basis of merit. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or any other characteristic under federal, state, or local law. If you require an accommodation during the job application process, please notify your recruiter.
Compensation Range: $160K - $210K
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What We Do
The market for cutting-edge ML compute is broken. Startups, researchers and even big AI labs are scrambling to buy or rent access to the latest chips for ML training. But demand far outstrips supply, and what’s available is only accessible to the well-resourced, placing an artificial damper on innovation.
To solve this challenge, we've launched Voltage Park, and we’re on a mission to make machine learning infrastructure accessible to all, from large enterprises and research universities, to seed-stage startups and nonprofits.
With around 24,000 NVIDIA H100 GPUs, the Voltage Park cloud is one of the most powerful collections of cutting-edge ML compute in the world. Our clusters consist of 80GB H100 SXM5 GPUs fully interconnected with 3.2T InfiniBand.
Why Work With Us
You’ll play a pivotal role as a member of the founding team that will change the face of machine learning infrastructure. As an early hire, you’ll have outsize influence in defining the company’s culture and ensuring mission success.
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Employees engage in a combination of remote and on-site work.