Product Designer at Aquarium Learning (S20)
We help ML teams improve their models by improving their datasets
Remote (US)
About Aquarium Learning

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.

About the role

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

  • Own your design process end to end, from initial flows and prototypes through final high-fidelity implementation.
  • Design user experiences that extend across multiple roles and offline interactions in customer organizations.
  • Work closely with the product and engineering teams on our product direction, strategy, and execution.
  • Establish and grow our internal design systems.
  • Help with hiring and establishing a design culture within Aquarium

What you should have

  • 3+ years as a Product Designer, or similar experience in a UX / UI / HCI related field
  • A customer-success focused mindset. You empathize with and understand customer problems, and come up with creative solutions to solve their problems.
  • Experience working within small teams, startups, or other rapidly growing organizations is a plus.
  • Experience designing for B2B, machine learning, data infrastructure, or similar products is a plus.

About Aquarium

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. ( 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 ( as a service.

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