Senior Software Engineer - GPU Sandboxes

nineDots.io; San Francisco (CA); On-site; USD 180,000 - 240,000; Full time

Benefits offered by this job

Equity offered

Relocation considered

Job summary

nineDots.io is hiring a Senior Software Engineer to build secure GPU sandbox environments and scalable GPU compute platforms. You will help define architecture and drive the stack from KVM/QEMU to CUDA lifecycle, ensuring isolation, security, and reliability for AI agents.

You’ll work across GPU virtualization, Linux kernel, and graphics drivers, with opportunities to influence software foundations and performance-critical paths in a fast-growing AI infrastructure company.

Qualifications

• Experience with virtualization and device isolation in production environments.
• Deep knowledge of GPU virtualization stacks and Linux kernel interactions.
• Ability to trace failures across hypervisor, drivers and hardware.

Responsibilities

• Define architecture and drive GPU sandbox platform design.
• Work across KVM/QEMU, VFIO, PCIe isolation, and CUDA lifecycle.
• Operate GPUs in production and troubleshoot end-to-end failures.
• Develop health monitoring, automated recovery, and performance improvements.
• Shape GPU-aware scheduling and bin-packing strategies.

Skills

KVM/QEMU

GPU passthrough

VFIO/IOMMU

PCIe isolation

NVIDIA drivers

CUDA lifecycle

Hypervisor debugging

Linux kernel

Go

Rust

C/C++

Production GPUs

Cloud hypersvisor

Job description

Give every AI agent its own GPU Senior Software Engineer – GPU Sandboxes

San Francisco onsite | Relocation considered | Equity offered

AI agents increasingly need more than access to a model. They need their own secure compute environment, with real GPU capacity available in seconds.

That is what you’ll help build.

You’ll join a well-funded AI infrastructure company creating isolated, stateful computers that agents can launch and control through an API. The GPU platform is still being built, so you won’t be maintaining someone else’s architecture you’ll be instrumental in defining how it works.

You’ll work deep in the stack across:

KVM/QEMU and GPU passthrough

VFIO, IOMMU and PCIe isolation

NVIDIA drivers and CUDA lifecycle

VM boot paths, snapshots and cold-start performance

GPU-aware scheduling and bin-packing

Health monitoring, failure detection and automated recovery

Security boundaries across shared hosts

They need someone who has built or debugged the underlying virtualization, isolation or device layer in production. KVM/QEMU, Cloud Hypervisor or Firecracker would be ideal, although similarly deep experience with crosvm, Kata Containers, Xen or another hypervisor can translate.

You should also have operated GPUs in production and be comfortable tracing difficult failures across the hypervisor, Linux kernel, drivers and physical hardware.

Go, Rust or C/C++ can all work.

This is rare, high-impact systems engineering with very little existing playbook. You’ll have genuine influence over the architecture, technical direction and foundations of a GPU platform being built for the next generation of AI agents.

Onsite in San Francisco, with relocation considered and equity offered.