Systems Engineer Intern

Dedalus Labs•San Francisco, CA

7d•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 distributed storage, virtualization, orchestration, networking, scheduling, and runtime infrastructure for long-running AI agents. We’re looking for unusually high-potential engineers who want to learn how reliable systems are designed, built, broken, and improved. This is a paid, full-time, approximately three-month internship based in San Francisco. Applications remain open on a rolling, year-round basis. When we meet an exceptional or unusually high-slope engineer, we can invite them to join the team for a season. You’ll work directly alongside Dedalus engineers on real infrastructure, not a disconnected intern project. You may shadow experienced engineers, but you’ll also be expected to take ownership, write production-quality code, investigate difficult problems, and explain your decisions. Interns who demonstrate exceptional technical ability, judgment, ownership, and mutual fit may be considered for full-time roles.

Requirements

Strong programming fundamentals, including data structures, concurrency, memory, and performance.
Experience building and debugging software in Rust, Go, C, C++, or another systems-oriented language.
Exposure to systems through coursework, research, open source, internships, or self-directed projects.
Curiosity about how operating systems, networks, storage, and runtimes work beneath their abstractions.
The ability to investigate difficult bugs, reason clearly about tradeoffs, and communicate what you learn.
High agency, fast learning, thoughtful responses to feedback, and end-to-end ownership.

Nice To Haves

Rust is preferred but not required.
A storage engine, scheduler, database, runtime, compiler, operating system, hypervisor, container system, or distributed service you built.
Experience with virtualization, sandboxing, containers, or hypervisors.
Familiarity with distributed storage, replication, consensus, or consistency models.
Experience profiling or optimizing latency, throughput, memory usage, or resource consumption.
Meaningful contributions to systems-focused open-source projects.
Systems research accompanied by a working implementation.
Technical writing that clearly explains architecture, failure modes, and engineering tradeoffs.
A project where you encountered a difficult failure and can explain how you diagnosed it.
Taste: You know the difference between code that merely works and code that’s built to last. You care about elegant engineering, good abstractions, and understanding why systems behave the way they do.

Responsibilities

Work directly alongside Dedalus engineers on real infrastructure.
Take ownership of tasks and projects.
Write production-quality code.
Investigate difficult problems.
Explain your decisions.
Build core compute and runtime primitives for long-running AI agents.
Develop virtualization, sandboxing, and isolation systems for secure multi-tenant workloads.
Create persistent state, storage, snapshotting, and recovery mechanisms.
Design low-latency scheduling and resource-management systems.
Implement networking and runtime infrastructure across the agent execution path.
Develop profiling, debugging, benchmarking, and performance tooling.
Write production-grade systems software in Rust, Go, C, or C++.