allyourbasepair
allyourbasepair on Hacker News
https://github.com/TimothyStiles/poly
A large part of my project's community are devs that want to get into the field but can't tolerate the ridiculously low pay, laughably bad management, disrespect, and what amounts to 40+ years of technical debt that's endemic to biotech software.
I've had companies here in the Bay Area offer me 100K a year with a straight face. I've had companies during interview tell me they're looking for someone to help, "set up GitHub". I've seen job listings for low paid web dev positions require applicants to have PhDs.
The reality is that except for a growing handful of places management straight up won't know the difference between IT and software engineers. It's what I call the naive buyers problem.
The demand for software engineers in biotech is generated by naive buyers that don't know what they need, why they need it, or how to get it.
Benchling and Recursion Pharmaceuticals have reputations in the industry of paying, "standard software salaries". So do the research divisions at places like deepmind/microsoft/google but in my experience there's even new multi-billion dollar institutes where senior management has never even heard the term devops.
Most places advertise for "data scientist", positions or some analog, instead of software engineers. This is mostly because upper management has never met an actual practicing software engineer in a professional setting. Many come from academia where the culture and work requirements heavily disincentivize standard software engineering practices.
It's also not uncommon for a biotech company to either have a very under qualified CTO whose main programming experience is what they learned doing ML research like stuff during their PhD or not even have one at all which has huge downstream consequences.
This week a software engineer trying to make the switch to biotech actually DM'd me to ask why they were seeing a ton of data science / ML job positions but no software engineering / devops positions.
They were worried that these companies were trying to save on costs by forcing their data scientists to create infrastructure but it's actually worse than that. Most of these companies aren't even aware that there's supposed to be infrastructure.
Despite all of this the future is looking better and I'm starting to find new companies and positions that are well... reasonable. I learned about this thread from a friend at a party last night that works at one of these companies. There's a small, strong new wave of companies and developers out there pushing biotech software forward. Hopefully some (including myself) make it big while pushing the idea that better tech equals better biotech.
https://github.com/TimothyStiles/poly
Goal is to have a suite of packages and databases that can be used to design entirely novel proteins, metabolic pathways, and DNA constructs at scale because right now that software ecosystem just doesn't exist.
1. Fast execution 2. Strong devops ecosystem 3. Easy to learn syntax 4. Ships binaries for as many systems as possible.
Julia wasn't really considered because last time I used Julia (which granted was a while ago) there wasn't any support for binary compilation targets and it didn't have a strong devops ecosystem. Also, it's more of a scripting language than something you'd write stable, deployable code in.
Rust beat out Go on speed and tied for shipping binaries but was, way, way harder to learn and Go was still leagues ahead when it came to packages and tooling.
If I remember correctly C is typically 3x faster than Go and 2x faster than Rust. That small decrease in execution time doesn't matter in a majority of use cases but Go is way faster and easier to learn and develop in so it ultimately won.