Aquarium Learning (S20)
We help ML teams improve their models by improving their datasets

Product Manager

San Francisco, CA
Full-time
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 Manager on the Aquarium team, you’ll work closely with the engineering, design, sales, and marketing teams to define and build our core product. 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.

You will participate in all stages of product development: initial conversations with users, roadmap planning, development, and ultimately delivering products that make all of our customers more successful. The products you will work on span across a Python client, a full featured web app, and integrations into critical parts of the ML tooling stack.

You will talk to corporate customers across a variety of company sizes and industries across the world. We’re looking for Product Managers who can think holistically about the problems being faced by machine learning teams, identify both the clear incremental improvements and the larger strategic initiatives we should be working on to solve those problems, and drive execution among a cross-functional team. This role demands strong collaboration skills, sharp product sense, and business acumen.

What you will do

  • Work closely with stakeholders (both internal and external) to set and communicate product strategy, vision, and roadmap.
  • Build a deep understanding of the machine learning space and the needs of machine learning teams. Identify the most important problems to solve. Drive feature development from start to finish.
  • Own the success of products after launch, defining success metrics and incorporating user feedback into future plans.
  • Collaborate with the design and engineering teams to deeply understand trade-offs and choices made. Collaborate with the sales and marketing teams to understand and optimize for business impact.
  • Help shape the culture and set the tone for future hires!

What you should have

  • 4+ years of professional product management experience.
  • Excellent written and communication skills.
  • Ability to synthesize large amounts of information from internal and external stakeholders into a clear and organized list of priorities.
  • Experience delivering a product for technical users, such as software engineers or data scientists.
  • Experience working within small teams, startups, or other rapidly growing organizations.
  • Experience influencing product direction and others around you.
  • Previous experience with B2B SaaS products or machine learning is a plus.

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 and more consistently 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.

Technology

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.

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