About You:

• You're excited to design a service architecture that orthogonalizes the critical feedback loops that entangle our code, data, models, and products.


• You're tired of organizations treating DevOps like an individual role instead of a company-wide practice.


• You're a networks/containers nerd who will turn us into networks/containers nerds.


• You've witnessed the pains that result from fitting square AWS-provided-solution pegs into round in-house-problem holes. Conversely, you've also seen how NIH syndrome can drive teams down a rabbit hole whose endpoint is a shallow reproduction of an existing AWS solution that could've just been employed in the first place.


• You are familiar with the many idiosyncrasies of storing, streaming, and analyzing large volumes of dense signal data in the cloud (e.g. audio, video, domain-specific sensor data, etc.).


• You have a battle-tested workflow for debugging performance issues and selecting the layer of the stack that actually merits optimization.


• You optimize in pursuit of lower human costs (temporal and/or fiscal), not lower machine costs.


• Our data science team makes heavy use of the Julia language. This quarter, we're tackling model evaluation as a CI process, pushing >70TB of signal data (and our processes for manipulating it) into AWS, and developing a browser-based viewing/analysis application for our signal data.