Aquarium Learning
We help ML teams improve their models by improving their datasets
About Aquarium Learning
ML models are only as good as the datasets they're trained on, and that means that most improvement to model performance comes from improvement to the quality and diversity of their datasets.
Our tooling makes it easy for ML teams to find anomalies + failure patterns in their datasets and fix these problems by editing / adding the right data. So the next time you retrain your model, it just gets better.
Company details
Funding
Total raised
$31.3M
Last stage
Series A
Investors
Founders
Quinn Johnson
Quinn is an engineer/manager who picked a *fantastic* time to co-found a company making deep learning pipelines that improve themselves. Before that he was at Ouster (leading data engineering / data viz), Cruise Automation (leading ML data engineering + labeling), and Graphistry (1st engineering hire, so a bit of everything). Working on self-driving cars has given him an irrational hatred for trees and shrubbery.
LinkedIn
Peter Gao
Early employee (#18) at Cruise, where he built deep learning systems for self-driving cars. Previously did deep learning research at UC Berkeley and interned at Pinterest and Khan Academy.
LinkedInWhat happens next.
No applications, no recruiter spam. Just the intro.
Confirm the fit
A few questions to make sure this role is the right shape for you. Two minutes.
I pitch you to the company
I write the intro, send it to the founder, and handle the back-and-forth.
A meeting lands on your calendar
If they’re a yes, I book the chat. You show up — that’s the whole job-hunt.