Job Overview
Job description
- Location:
- Remote, United States
- Work arrangement:
- Remote
Role Summary
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.
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.
Requirements
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
- Remote in a US timezone; US preferred; cannot sponsor visas at this time
- Role:
- Senior Software Engineer - Cloud Platform
- Job Type:
- FullTime
Company profile

Shadeform
shadeform.comShadeform runs a GPU cloud marketplace for AI development. It lets teams develop, train and deploy AI models in any cloud environment, giving them access to on-demand GPUs across multiple GPU clouds. Users can deploy high quality GPUs from 30+ clouds worldwide at competitive prices and scale ML inference for their workloads through a single platform.