Description

Experience building and maintaining training and inference infrastructure, with an understanding of what it takes to move from concept to production

A strong mathematical background; Good candidates will be excited about things like optimization theory, regularization techniques, linear algebra, and the like

A passion for keeping up with the state of the art, whether that means diving into academic papers, experimenting with the latest hardware, or reading the source of a new machine learning package

A proven ability to create and maintain an organized research codebase that produces robust, reproducible results while maintaining ease of use

Expertise wrangling an ML framework – we're fans of PyTorch, but we'd also love to learn what you know about Jax, TensorFlow, or others

An inventive approach and the willingness to ask hard questions about whether we're taking the right approaches and using the right tools

Key Skills

Education

Bachelor's degree