Systems Engineer

What we're looking for We need a systems engineer with depth in OS/kernel internals, virtualization, file systems, or distributed infrastructure who has owned technically ambitious projects end-to-end — in production, open source, or research. You should be comfortable working at the lowest layers of the stack (kernel, hypervisor, storage, networking) and have a verifiable body of work that demonstrates genuine depth.

Bonus points if you have experience with GPU infrastructure, AI/ML systems, or distributed storage/consensus.

What you'll do Build core compute and runtime primitives for long-running, stateful AI agents — including virtualization, sandboxing, and isolation systems for secure multi-tenant workloads Take ownership of entire subsystems end-to-end (storage, scheduling, networking, virtualization) and ship them to production at an 8-person team where your work matters immediately Work closely with the CTO and engineering team to design and implement persistent state, snapshotting, and recovery mechanisms for always-on agent workloads Reduce sandbox startup latency to sub-50ms and optimize performance across every syscall and scheduling decision in the critical path Design and build distributed storage layers, schedulers, and networking infrastructure that will scale across data centers globally Profile, debug, and benchmark systems under real-world scale, latency, correctness, and reliability constraints — measuring before optimizing, then optimizing relentlessly Contribute to the longer-term roadmap including on-prem GPU clusters and model training infrastructure as Dedalus expands beyond cloud compute