| Wow. This is enormously impressive. I currently work for a small startup based out of Denmark in the renewable energies space (wind turbines) developing web based tools to assist in our ML operations. With a lifelong obsession with cosmology and astronomy, and perhaps even more applicably relevant; our own human advancement to and into the stars, I have increasingly become more and more inclined to the notion of further developing my current skillset with the eventual goal of transitioning to the space industry. My recent experience and exposure to renewable energies has given me massive insight to just how important companies like you guys are to furthering humanity’s progress. My question to you all regarding your technology, is how you manage what I imagine to be extraordinarily large, rich, and complex datasets that must vary between use cases (you mention hotels, debris removal, etc.). The data between these use cases must vary in structure— how is it normalized/standardized to work with your pipeline(s)? The commonality I see (as a fairly novice layman in terms of space technology) is of the rocket propulsion, orbiting, and payload delivery kind, but I’m sure the data it is far more nuanced and goes far beyond that. Furthermore, is any sort of machine learning applied on your side, perhaps in some sort of statistical analysis / metric reporting? I’m going to definitely keep an eye on you all at Epsilon3. Perhaps you will be looking for more engineers with web dev, data, ML, and cyber/info security experience in the future! Huge props. I can tell there is an extraordinary amount of innovation involved with this venture. Excited to see where you all go with this =) |
We have been very thoughtful to build as flexible a framework as possible to support all those various use cases you said (not only in our user interface but also our API). We want to give end-users across the continuum of use cases the tools they need to be able to make Epsilon3 as useful for them as possible.
We have a ton of ideas on applying ML on our side for exactly those use cases you described (metrics, analysis, reporting) but also for anomaly detection, error handling/risk reduction, and continuous improvement.
We have our job openings posted at https://angel.co/epsilon3/. We're always on the lookout for strong full-stack software engineers.