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Beacon Biosignals | Lead Cloud Ops Engineer

(Boston, MA | Onsite Available, Remote Friendly | Full Time)

We're 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. We're adamant that...

  • ...product development goes off the rails without rapid, early feedback from real users.
  • ...honest, frequent, and open communication are more significant contributors to software development than technical wizardry.
  • ...diversity is an integral part of strong engineering culture. Differing viewpoints are borne from differing backgrounds, and lack of diversity contributes to stagnation.

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've been tackling model evaluation as a CI process, pushed 70TB of signal data into AWS, and have been developing a browser-based viewing/analysis application for our signal data. Come help us make the right decisions!

Contact if interested.

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