Systems Engineer

Dedalus Labs•San Francisco, CA

43d•Onsite

About The Position

Dedalus Labs is an AI research neolab building infrastructure for AI agents. We’re building the persistent compute layer that powers the next generation of autonomous software. Our platform spans virtualization, distributed systems, storage, networking, orchestration, and low-level runtime infrastructure. Every millisecond, every sys-call, every scheduling decision matters. We’re looking for systems engineers who enjoy understanding computers all the way down.

Requirements

Fluency in Rust (preferred), Go, C/C++, or similar systems languages.
Strong software engineering fundamentals.
Good understanding of operating systems, distributed systems, or networking concepts.
Ability to reason about performance, reliability, concurrency, and debugging.
Comfortable navigating large systems with many moving parts.
High agency and excellent engineering judgment.
Strong communication and collaborative instincts.

Nice To Haves

Experience with Kubernetes or modern cloud infrastructure.
Experience building distributed systems in industry, research, or open source.
Familiarity with distributed storage systems.
Understanding of consistency models and consensus.
Experience with virtualization, containers, hypervisors, or Firecracker.
Kernel, operating systems, or low-level runtime experience.
Contributions to systems-focused open source projects.
Experience with performance engineering and systems optimization.
Background in systems research, networking, databases, distributed computing, or related fields.
Experience operating production infrastructure at scale.

Responsibilities

Building distributed infrastructure for large-scale agent workloads.
Developing virtualization and sandboxing systems.
Creating persistent compute and storage primitives.
Designing scheduling, orchestration, and reliability systems.
Building runtime infrastructure for long-running AI agents.
Developing internal developer platforms and tooling.
Operating production systems under real-world scale, latency, and reliability constraints.