
Job Overview
Location
Remote
Job Type
Full-time
Category
Engineering
Date Posted
April 10, 2026
Full Job Description
📋 Description
- • Senior Network Optical Engineer responsible for designing, deploying, and operating optical and physical layer infrastructure for large-scale, high-performance network environments at TensorWave, Inc.
- • Day-to-day tasks include designing and implementing optical network infrastructure, defining and maintaining standards for fiber plant and cabling systems, selecting and validating optical transceivers and connectivity solutions, troubleshooting physical layer issues such as signal loss and link instability, collaborating with network engineering teams to ensure integration between optical and packet layers, developing documentation for structured cabling and optical deployment practices, validating link budgets and optical performance, and supporting data center builds, expansions, and migrations with a focus on physical layer reliability.
- • TensorWave, Inc. is a cloud platform company dedicated to delivering seamless, secure, reliable, and resilient AI compute at scale by eliminating infrastructure barriers so innovators can focus on breakthroughs rather than stack management.
- • The role offers the opportunity to deepen expertise in optical networking, influence infrastructure standards for high-scale AI workloads, work with cutting-edge optical technologies (400G/800G+), and contribute to the reliability and scalability of AI-optimized data center environments.
Skills & Technologies
About TensorWave, Inc.
TensorWave develops and operates an AI accelerator cloud built on AMD Instinct GPUs, targeting large-scale model training and inference. The platform offers on-demand and reserved compute with high-bandwidth memory, InfiniBand networking, and container orchestration, delivered through a web console and API. Designed for generative AI, LLM fine-tuning, and HPC workloads, the service emphasizes AMD performance at competitive pricing, supported by 24/7 operations teams. Based in Las Vegas, Nevada, the company serves hyperscalers, research labs, and enterprises needing GPU capacity beyond NVIDIA ecosystems.
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