I believe that a distributed smart grid that uses green/renewable energy is necessary for reliability in the face of extreme weather conditions, infrastructure cost reductions via reduction in size/use of unsafe HV transmission lines (see: CA fires), and most importantly to head-off the climate disaster that we're already seeing manifest today, so I want to keep working in those areas.
Location: USA
Remote: Yes, preferred.
Willing to relocate: Will discuss for extraordinary opportunities.
Technologies: [I'm comfortable learning new technologies and learn quickly, so if you work with a technology I didn't write down here, email me anyways!] I currently work with modern Python (with types, async libraries, newer runtimes, etc.) and SQL as far as languages go, but I also do personal stuff in Go, Lua, and others. I have recently worked with several differnent databases, such as Postgres, AWS Athena, Redis, FoundationDB, Elasticsearch, and SQLite. I don't do much devops, but I have experience with containers and other modern methods, and some of my own simpler methods that can work at massive scale (local data + compiled binary distribution + many many servers = massive scale, for the right applications).
Résumé/CV: [Here's a short description of what I currently work on and wish to continue doing; I can send a full resume upon request.] I work on computationally efficient energy efficiency software that optimizes the energy use of and then commands devices at a site. For example, I wrote a software algorithm which reduces HVAC peaks when a bunch of HVACs turn on at once by preventing that through an energy budget and some other magic. I also wrote algorithms to manage peaks using a battery by setting a goal for the energy use for a particular billing interval and then discharging the battery based on that goal and then constantly re-evaluating that goal based on new information. I also built a system which queried telemetry from a fleet of thousands of distributed energy resources every minute, determined if there were any deviations from normal/acceptable conditions, then alert the operations team (my alerting software ran in ~15 seconds in a "0.25 CPU / 1024MB RAM" kubernetes pod and replaced other software which did less alerts, took ~1 hour to do it, and took literally 10x the resources).
Email: Please initially contact me with my HN masked email, for my own spam protection, and I will reply from my normal account: [soft.oil7342 (a) fastmail (d) com]
Location: USA
Remote: Yes, preferred.
Willing to relocate: Will discuss for extraordinary opportunities.
Technologies: [I'm comfortable learning new technologies and learn quickly, so if you work with a technology I didn't write down here, email me anyways!] I currently work with modern Python (with types, async libraries, newer runtimes, etc.) and SQL as far as languages go, but I also do personal stuff in Go, Lua, and others. I have recently worked with several differnent databases, such as Postgres, AWS Athena, Redis, FoundationDB, Elasticsearch, and SQLite. I don't do much devops, but I have experience with containers and other modern methods, and some of my own simpler methods that can work at massive scale (local data + compiled binary distribution + many many servers = massive scale, for the right applications).
Résumé/CV: [Here's a short description of what I currently work on and wish to continue doing; I can send a full resume upon request.] I work on computationally efficient energy efficiency software that optimizes the energy use of and then commands devices at a site. For example, I wrote a software algorithm which reduces HVAC peaks when a bunch of HVACs turn on at once by preventing that through an energy budget and some other magic. I also wrote algorithms to manage peaks using a battery by setting a goal for the energy use for a particular billing interval and then discharging the battery based on that goal and then constantly re-evaluating that goal based on new information. I also built a system which queried telemetry from a fleet of thousands of distributed energy resources every minute, determined if there were any deviations from normal/acceptable conditions, then alert the operations team (my alerting software ran in ~15 seconds in a "0.25 CPU / 1024MB RAM" kubernetes pod and replaced other software which did less alerts, took ~1 hour to do it, and took literally 10x the resources).
Email: Please initially contact me with my HN masked email, for my own spam protection, and I will reply from my normal account: [soft.oil7342 (a) fastmail (d) com]