Reproducibility First
Every claim is backed by code. Our open-source packages implement the methods described in our papers. If you can't reproduce it, it doesn't count.
About
Building trust in autonomous AI.
Rotalabs is the open-research division of Rota, Inc. As AI agents begin to reason, act, and coordinate, the question shifts from "what can AI do?" to "can we trust what it does?" We answer it with reproducible methods, public code, and rigorous evaluation — work that stands on its own merits.
2024Founded
12Open-source packages
9Research areas
100%Open source
Our approach
Research principles.
A few principles shape everything we build and publish.
Adversarial Thinking
We assume AI systems will be deployed in adversarial conditions — gamed benchmarks, strategic underperformance, coordinated deception — and design our methods to hold up under those assumptions.
Statistical Rigor
LLM evaluation is plagued by noisy benchmarks and overfit leaderboards. We ground our evaluation in statistical testing, distributional analysis, and formal verification where possible.
Open by Default
Research is published openly and code is open-source. Trust research must itself be trustworthy — and that starts with transparency.
Our people
The work speaks first.
Rotalabs was founded by engineers and researchers with backgrounds in large-scale distributed systems, cloud infrastructure, and AI/ML — including prior experience at Google and in production ML systems serving hundreds of millions of users.
We combine deep systems engineering with applied AI research: we've built and operated infrastructure at hyperscale, and we bring that same rigor to AI reliability and trust.
We are currently in stealth and will share more about the team as we move into our next phase. In the meantime, our research and open-source work speak for themselves.
Structure
The Rota ecosystem.
Rota, Inc. is a Delaware C-Corporation headquartered in San Francisco. Research and products are organized across three divisions; Rotalabs is the open-research one.
Our research is open. Applied work builds on published methods.
Careers
Join us.
We're looking for people excited about AI reliability, evaluation, and systems engineering. If you want to work on hard problems in the open, we'd like to hear from you.
Research
AI Safety Researchers
Interpretability, adversarial ML, formal verification, multi-agent trust. Publish openly and build tools from your research.
Apply → Engineering
Systems Engineers
Build trust infrastructure at scale — Python, Rust, distributed systems, GPU kernels. Production ML experience preferred.
Apply → Open Source
Contributors
All our packages are open source. Issues, PRs, and discussions welcome across 12 repositories on GitHub.
GitHub → Get in touch