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# Senior Software Engineer, GPU Sandboxes

- **Company:** DAYTONA PLATFORMS INC
- **Location:** San Francisco, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Systems Engineering, C++ (Programming Language), Cloud Computing, Nvidia CUDA, Computer Programming, Software Debugging, Linux, Hypervisor, Kernel-Based Virtual Machine, Linux Kernel, PCI Express, Quick EMUlator (QEMU), Software Engineering, Graphics Processing Unit (GPU), Caching, Hardware Infrastructure, Virtual Agents, Serverless Computing
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/senior-software-engineer-gpu-sandboxes-san-francisco-ca--41136d03-2726-4ce0-ab35-b986d52e73cc

## About the Role

Software Engineering, GPU (Graphics Processing Unit), GPU (Graphics Processing Unit), Application Programming Interface (API), Economics, Hypervisors, Artificial Intelligence (AI), Kernel Programming, Team Lead/Manager, CUDA (Compute Unified Device Architecture), 5+ years of systems engineering in production environments

Hands-on depth in Linux virtualization internals: KVM, QEMU, libvirt, VFIO, IOMMU

Strong Go, Rust, or C/C++ and real Linux kernel-adjacent debugging skills

Experience operating GPUs in production (NVIDIA data center hardware preferred)

You ship, you own your services, and you like hard problems more than big teams

Nice to have

Contributions to QEMU, KVM, Firecracker, Cloud Hypervisor, or similar

## Description

You will build the runtime that gives every AI agent its own GPU. Full passthrough, hard isolation, serverless economics. This is deep systems work: hypervisors, kernels, drivers, and the orchestration layer that makes an GPUs appear behind an API call in seconds.

What You\'ll Do

Build and harden the GPU sandbox runtime: KVM/QEMU, VFIO passthrough, IOMMU group management, guest driver and CUDA stack lifecycle

Attack cold-start latency across the stack: image caching, VM boot paths, driver init, snapshot restore

Build fleet-level systems: GPU-aware scheduling and bin-packing, health monitoring, failure detection, automated remediation

Implement pause, snapshot, and fork semantics for GPU-attached workloads

Own security-relevant surfaces: device isolation, side-channel considerations, tenant boundaries on shared hosts

Debug the ugly stuff: PCIe errors, IOMMU faults, driver crashes, thermal throttling, the works