Principal Network Engineer
Job Description
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About the Role
We’re looking for a Principal Network Engineer to join our team during an exciting phase of growth. In this role, you’ll be responsible for owning the front-end network architecture for large-scale AI and GPU-accelerated infrastructure, working closely with cross-functional partners to support business objectives while upholding our standards for excellence, collaboration, and impact.
What You’ll Do
- Own front-end network architecture - DCI, edge, ingress/egress, and control-plane networks
- Architect and operate large edge and service networks
- Design scalable Ethernet architectures
- Define routing, segmentation, and isolation strategies
- Lead hands-on deployment, validation, and troubleshooting in new data centers
- Define and maintain reference architectures, standards, and long-term growth models
- Own relationships with network carriers and service providers
- Work in collaboration with the platform team to design and deliver network solutions for Kubernetes-centric use cases
- Partner closely with the Back End Network Principal to define clean interface boundaries between front-end and RDMA back-end fabrics
Who You Are
Required Qualifications
- Bachelor of Science in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience
- 10+ years data center networking experience
- Proven experience with very large Ethernet fabrics and large-scale edge networks
- Strong hands-on experience with BGP, traffic engineering, and high-availability designs
- Experience with 100G–400G+ Ethernet environments
- Familiarity with optical standards and transceiver types (e.g., 100G/400G/800G, SR/LR/ER, DWDM)
- Demonstrated experience working with carriers providers to deliver production connectivity
- Multi-Vendor Experience - Juniper, Cisco, Arista, Whitebox
- NOS Experience - Junos, IOS/IOS-XE, NX-OS, EOS, SONiC
Preferred Qualifications
- Automation or scripting experience in Python, GO, Bash, or equivalent
- AI platform or GPU cluster environments
- Multi-tenant or customer-facing platforms
- Strong familiarity with Kubernetes networking concepts
- Exposure to network automation and programmability
- 100G+ environments
- AI, GPU, or HPC exposure
What We Offer
- Stock Options
- 100% paid Medical, Dental, and Vision insurance for Employees
- Company Health Savings Account Contributions
- 100% paid Short Term and Long Term Disability Insurance for Employees
- Life and Voluntary Supplemental Insurance Options
- Other Insurance Options, such as Pet & Legal Insurance
- Various Supplementary Health Benefits, such as discounted Virtual Healthcare Appointments and Serious Illness Support
- Flexible Spending Account
- 401(k)
- Employee Assistance Program
- Flexible PTO
- Paid Holidays
- Parental Leave
- Other In-Office Perks
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View Company ProfileTensorWave is a premier, enterprise-grade cloud infrastructure provider engineered to orchestrate massive-scale artificial intelligence (AI) and high-performance computing (HPC) workloads. Operating as a high-velocity digital ecosystem, the company eliminates the operational friction and supply chain bottlenecks of legacy hyperscalers by exclusively deploying advanced AMD Instinct™ accelerators—including the MI300X and MI325X—across highly optimized bare-metal environments. Moving beyond the rigid memory constraints of traditional GPU cloud models, TensorWave provides industry-leading capacity with up to 288GB of HBM3e per accelerator, directly tackling the immense requirements of next-generation Large Language Models (LLMs) and complex machine learning training clusters. Under the hood, their UEC-ready networking architecture and direct liquid cooling systems seamlessly integrate into existing AI pipelines, ensuring ultra-low latency inference and blistering training speeds. What sets TensorWave apart is its uncompromising dedication to open-source democratization and performance accessibility; by bridging the gap between top-tier compute resources and massive cost-efficiency, the platform empowers scaling enterprises to radically accelerate AI innovation without the burden of building internal hardware infrastructure.
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