We help ML teams improve their models by improving their datasets
As the first Product Designer on the Aquarium team, you will be responsible for driving design on our core product: an application for machine learning teams to visualize their datasets and collaborate on operational workflows.
With a machine learning product, we have the challenge of bridging deeply technical concepts with very human processes. Our users range from PhDs in computer vision, to experts in their niche industry, to operations team members focused on efficiency. Their problems are equally varied – agriculture, robotics, social media, insurance, and recycling are a handful of the industries our customers work in. The ideal candidate is a designer with strong systems thinking and communication skills, who can think beyond pixels and design for the full offline experience.
You will also help drive design across the company, from establishing internal design systems to helping build a strong design culture. Your work will directly enable machine learning teams across all industries to deploy machine learning models that work in production, from small startups to large enterprises.
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 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.