Aquarium helps deep learning teams improve their model performance by improving their datasets.
A model is only as good as the dataset it’s trained on. We help teams find problems with their datasets + models and fix them by editing / adding data to their datasets.
As the Head of Marketing, you will help craft Aquarium's story, communicate it to the world, and drive the growth of the business. At Aquarium, we work with a variety of companies applying machine learning to their industries. Here you'll get to explore the world of AI, deeply understand the challenges of getting machine learning to work in the real world, and help our customers change the world. You'll work closely with a cross functional team to build an amazing product and establish a position as a market leader. You'll help build a world-class marketing team and grow a lasting business.
What you will do
What you should have
Machine learning is eating the world. However, though it’s easier than ever to build a prototype of an ML system, it’s still extremely difficult to build, maintain, and improve ML systems in production to solve real-world problems. Aquarium helps teams ship better ML models faster to enable the next generation of revolutionary AI applications.
Aquarium is backed by top investors including Y Combinator and Sequoia Capital. Our customers span many industries, from robotics to agriculture to construction. We’re looking to grow our team with awesome people who’ll shape the future of Aquarium -- both as a product and as a company.
Aquarium’s technology relies on letting your trained ML model do the work of guiding what parts of your dataset to pay attention to.
For example, Aquarium finds examples where your model has the highest loss / disagreement with your labeled dataset, which tends to surface many labeling errors (ie, the model is right and the label is wrong!).
Users can also provide their model's embeddings for each entry, which are an anonymized representation of what their model “thought” about the data. The neural network embeddings for a datapoint encode the input data into a relatively short vector of floats. We can then identify outliers and group together examples in a dataset by analyzing the distances between these embeddings. We also provide a nice thousand-foot-view visualization of embeddings that allows users to zoom into interesting parts of their dataset. (https://youtu.be/DHABgXXe-Fs?t=139). We heavily use React, WebGL, Python, and Apache Beam in our day-to-day work.
Think about this as a platform for interactive learning. By focusing on the most “important” areas of the dataset that the model is consistently getting wrong, we increase the leverage of ML teams to sift through massive datasets and decide on the proper corrective action to improve their model performance.
Our goal is to build tools to reduce or eliminate the need for ML engineers to handhold the process of improving model performance through data curation - basically, Andrej Karpathy’s Operation Vacation concept (https://youtu.be/g2R2T631x7k?t=820) as a service.