Our super early-stage startup is seeking an individual to lead the development/operations of our AWS infrastructure, and along the way teach us all how to deliver more robust software.

About Us:

Despite its significant potential for improving patient outcomes, brain monitoring is still not easily accessible or interpretable in clinical settings. We're going to fix that, and we'd like you to help.

We're a semi-stealth-mode startup founded by numerical programmers, neuroscientists, and practicing neurologists who are committed to translating our best-of-breed clinical research from the lab into the ICU and ED. We're well-funded, well-connected, and own a well-labeled set of brain data amassed over the past decade at some of the most prestigious medical institutions in the world. This dataset is, as far as we know, the largest of its kind in existence. We intend to put it to good use.

Our team is composed of neuro-experts, open-source enthusiasts, audio/DSP engineers, programming language nerds, and generally easy-going (but dedicated!) folks.

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 believe that diversity is an integral part of strong engineering culture, and that lack of diversity contributes to stagnation.

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. Come help us make the right decisions!