Aquarium Learning

We help ML teams improve their models by improving their datasets

Software Engineer, Full Stack

Job Type
3+ years
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Peter Gao
Peter Gao

About the role

Aquarium is making it easier for teams to build and improve their ML models.

As a full stack software engineer, you will drive development of our core user-facing application. Our current tech stack is a modern, rich frontend experience, built on React/Typescript/WebGL on the frontend, with a primarily Python backend. In addition to building out a well-engineered application, you’ll also be heavily involved in product iterations. Machine learning projects involve many different people (ML Researchers, ML Engineers, Product Managers, Operations, etc.), and the right product will understand and support everyone involved.

What you will do

  • Drive product development of our user-facing web application, both through incremental improvement and entirely novel features.
  • Improve our existing technical foundations, and influence our technical direction and strategy.
  • Work closely with our customers to build the right product that lets them succeed.
  • Generally be a great person, and help set the tone for future hires!

What you should have

  • 2+ years of professional development experience.
  • Demonstrated skills with Javascript, and at least one modern framework (React, Vue, Angular, etc.). Bonus points for production python experience.
  • Experience with highly interactive or data intensive web applications.
  • The ability to work in an unstructured, self-directed environment.
  • A love for building, especially novel product experiences.
  • Care and empathy for users. We only exist to make our customers successful.
  • Bachelor’s degree in Computer Science or a related field, or equivalent industry experience.

Why you should join 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.

Aquarium Learning
Team Size:13
Location:San Francisco
Peter Gao
Peter Gao
Quinn Johnson
Quinn Johnson