Dedalus Labs · San Francisco
Mechanical & AerospaceEngineeringFullTimeposted 2mo ago
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 engineers who enjoy designing systems that continue working long after individual machines fail.
Think distributed systems are one of computer science’s most beautiful subjects.
Care deeply about consistency, fault tolerance, and system correctness.
Enjoy designing systems before writing them.
Think latency, throughput, and reliability are all product features.
Read systems papers because they’re genuinely interesting.
Have strong opinions about storage engines, consensus algorithms, scheduling, or distributed architecture.
Believe simple systems are usually harder to build than complicated ones.
Think every abstraction has a cost.
Measure before optimizing, then optimize relentlessly.
View infrastructure as a product for other engineers.
Are high agency and fiercely independent.
Say how things ought to be built, then build them.
Are a competitive teammate with a heart of gold.
Are hungry to learn, improve, and reflect deeply on feedback.
Go above and beyond in everything you do.
Distributed infrastructure for large-scale AI agent workloads.
Persistent compute and distributed storage systems.
Scheduling and orchestration platforms.
Virtualization and sandboxing infrastructure.
Reliable multi-tenant cloud systems.
Internal developer platforms and infrastructure tooling.
Production systems operating under real-world scale, latency, and fault tolerance constraints.
You might find yourself working on problems like:
Designing distributed storage systems for persistent agent state.
Building scheduling infrastructure that efficiently allocates compute across thousands of concurrent agents.
Improving reliability and fault tolerance across distributed infrastructure.
Designing virtualization and isolation systems for secure multi-tenant execution.
Optimizing bottlenecks across networking, storage, scheduling, and runtime layers.
Building infrastructure that makes operating AI agents dramatically simpler for developers.
Strong systems programming ability in Rust (preferred), Go, C/C++, or similar languages.
Deep understanding of distributed systems, operating systems, and concurrent programming.
Experience building distributed systems in industry, research, or open source.
Strong engineering judgment around performance, scalability, reliability, and debugging.
Experience with Kubernetes and modern cloud infrastructure.
Ability to design systems that remain reliable under production workloads.
High agency and excellent engineering judgment.
Experience with distributed storage systems.
Familiarity with consistency models, consensus algorithms, or replication protocols.
Experience with virtualization, hypervisors, containers, or Firecracker.
Kernel, operating systems, or low-level runtime experience.
Experience operating infrastructure at production scale.
Published systems research (NSDI, OSDI, SOSP, EuroSys, ATC, etc.).
Contributions to systems-focused open-source projects.
Experience with performance engineering and systems optimization.
You know the difference between a distributed system that works and one that continues working when everything goes wrong.
You care about elegant architecture, principled engineering tradeoffs, and building infrastructure that engineers trust.
In person in San Francisco.
We sponsor visas.
Relocation support available.
Competitive salary and meaningful equity.
Meals and office benefits included.
The first thing we look at is your GitHub.
Show us distributed systems you’ve built. Open-source infrastructure. Research. Storage engines. Schedulers. Consensus implementations. Infrastructure you’ve operated in production. Technical writing.
We care far more about systems you’ve built than years on your résumé.