Senior Software Engineer, GPU Sandboxes

Were representing a rapidly growing infrastructure company building the compute layer for AI agents. Its platform provides secure isolated instantly available sandboxes where AI agents can execute code interact with computers and run training workloads.

Following its Series A the company has already grown 10x and is now expanding its GPU infrastructure to give AI agents on-demand access to dedicated GPU compute with strong isolation and serverless economics. The engineering challenges sit deep in the systems stack spanning hypervisors Linux virtualization GPU drivers kernels scheduling and the orchestration infrastructure required to make dedicated GPUs available behind an API call within seconds.

This is a deeply hands-on systems engineering role for someone who wants to solve difficult infrastructure problems rather than work within a large engineering organization.

• Build and productionize the runtime powering GPU-backed sandboxes for AI agents and machine learning workloads.
• Develop virtualization infrastructure using KVM/QEMU VFIO passthrough and IOMMU isolation.
• Build systems for managing IOMMU groups PCIe devices guest drivers and CUDA stack lifecycles.
• Optimize GPU sandbox cold-start latency across image caching VM boot paths driver initialization and snapshot restoration.
• Build GPU-aware scheduling and bin-packing systems that maximize fleet utilization and improve serverless GPU economics.
• Develop fleet-wide GPU health monitoring failure detection and automated remediation infrastructure.
• Implement pause snapshot restore and fork capabilities for GPU-attached workloads.
• Design and harden security-sensitive infrastructure around device isolation tenant boundaries and shared-host environments.
• Investigate and resolve complex production issues involving PCIe errors IOMMU faults GPU driver crashes thermal throttling and hardware failures.
• Build tooling and automation for managing production GPU fleets.
• Own systems from initial architecture and implementation through deployment observability on-call and ongoing reliability.
• Work closely with the GPU Sandboxes Engineering Lead and other senior engineers on the platforms long-term technical architecture.

• 5 years of professional systems infrastructure virtualization or platform engineering experience.
• Deep hands-on experience with Linux virtualization internals including KVM QEMU libvirt VFIO and IOMMU.
• Strong systems programming experience with Go Rust C or C.
• Experience debugging Linux systems at or near the kernel driver virtualization or hardware boundary.
• Production experience operating GPU infrastructure ideally involving NVIDIA data center hardware.
• Strong understanding of virtualization hardware isolation PCIe devices and production Linux environments.
• Experience building or operating infrastructure where performance reliability and resource utilization are critical.
• Strong debugging ability across hardware and software boundaries.
• Comfortable owning production services including observability incident response and on-call.
• High ownership mindset and interest in solving difficult low-level technical problems in a small fast-moving team.

• Contributions to virtualization technologies such as QEMU KVM Firecracker Cloud Hypervisor or similar open-source systems projects.
• Experience with NVIDIA data center GPUs such as H100s or A100s.
• Experience using NVML or DCGM for GPU telemetry health monitoring and fleet management.
• GPU driver-level engineering or debugging experience.
• Experience implementing GPU checkpointing snapshotting or workload restoration.
• Background building serverless compute GPU clouds virtualization platforms or hyperscaler infrastructure.
• Experience with RDMA or other high-performance networking technologies.
• Familiarity with GPUDirect.
• Experience with NVLink multi-GPU interconnects or other multi-GPU infrastructure.
• Experience optimizing VM cold starts image caching scheduling or infrastructure density.
• Familiarity with infrastructure supporting AI agents reinforcement learning model training or inference workloads.

• Competitive base salary plus equity.
• Full-time position reporting to the Engineering Lead for GPU Sandboxes.
• Location options in San Francisco or Croatia.
• Opportunity to build deeply technical GPU infrastructure spanning hypervisors kernels drivers hardware and distributed orchestration.
• Join shortly after the companys Series A during a period of rapid growth with the platform already having grown approximately 10x.
• Work within a small senior engineering organization where individual engineers have significant architectural and production ownership.
• Opportunity to help solve emerging infrastructure problems around making dedicated GPU compute instantaneous isolated and economically viable for AI agents.