Senior Network Site Reliability Engineer
About Shadeform
Shadeform provides a unified platform for deploying and managing GPU infrastructure across cloud providers, neoclouds, and data centers. Our platform gives startups, inference providers, and enterprises a consistent way to access and operate GPU capacity through a single API and service layer.
About the Role
As a member of the Shadeform engineering team, your core responsibilities will be building and maintaining the Shadeform GPU platform and building out automated AI infrastructure services. Because of Shadeform’s unique position in the market, you will work on novel solutions to some of the biggest challenges in the GPU market. This role includes GPU provisioning, orchestration, virtualization, managing customer-facing infrastructure services, and building in multi cloud environments. You will own the systems that turn fragmented GPU capacity into a reliable, production-ready platform. You will have opportunities to test out bleeding edge GPU infrastructure and utilize the latest AI dev tools.
Preferred Experience
Operating and debugging with Kubernetes
Building and maintaining public facing cloud platforms and managing instance lifecycle
Building managed services and public APIs utilized by technical customers
General backend and infrastructure development
Technical Stack
Golang and Next.js; we expect everyone to be language agnostic
AWS / GCP
Kubernetes
Virtualization technologies such as KVM and Kubevirt
Location
Remote in a US timezone; US preferred; cannot sponsor visas at this time
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Shadeform operates a marketplace and control plane for GPU cloud infrastructure. Through one console and API, AI teams can compare availability and pricing and launch compute across dozens of vetted providers and regions. The platform removes the need to manage separate cloud accounts, quotas, integrations, support relationships, and deployment interfaces for each provider. Shadeform serves developers, startups, and enterprises that need flexible access to GPUs for training, inference, and other machine-learning workloads.
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