Consider working on genomics

So if one was financially independent and wished to write something open-source in that field, where would the highest impact be?

> the biologists first come up with the first concept of the tool, which is purely focused on ensuring good results. After this first iteration is completed, engineers then come in and rewrite the tool using modern engineering practices with things like speed and reliability in mind.

I think that is already accepted as good practice, and the way most people in the field work, which is part of the reason why the field is in this shoddy state right now. Because in reality, most of the time those engineers don't exist and it will never advance to the second stage, but will still be used regardless. And even if you manage to find an engineer for your team, the same problem exists in many layers down your stack.

As with most other kinds of software, the biologists should be treated as customers (or trained up to be skilled-enough engineers), as it is done in other disciplines. To create good accounting software you also wouldn't propose to have the accountant write the initial version of the software, would you?

> Many of the projects that are critical to the foundation of genomics are reaching or have eclipsed the ten-year mark. How much longer can we expect these individuals to single-handedly maintain these code bases?

What you propose sounds more like "hey, be the next idiot that commits to maintaining critical software for nothing", rather than any systemic change. The ugly secret of bioinformatics is the same one as in broader tech: Most of it runs on the backs of unpaid OSS maintainers (in this case a handful of motivated PIs that carve out some of their time for that).

If you want to have good software in the sciences, you first have to solve the OSS funding problem.

PS: the `user-select: none;` on your page is really annoying

From a genomics layperson with decent dev skills:

A. What are the broad and medium goals you work on?

B. What are your daily activities? How do they fir into (A)?

C. What does nonprofit genomics vs for profit look like from a revenue standpoint?

D. What specific technologies/stacks are you using?

E. The CRUD frontend+backend+database to serve users (and sell ads) is pretty ubiquitous in 'tech', with some branches. How does your field compare?

Even large corporations in this space pay relatively little for software engineers, and treat them with little importance.

I also experienced "software engineers" who had no idea what they were doing being given more credence because they had a PhD in some bio-related field. Oh, you got a PhD in some molecular aspect of some tiny piece of biology, and that makes you qualified to build big data systems? It did not. Apparently what that gives you is an adherence to reading decades old textbooks about database design. It was like working with a first year software engineering undergrad from twenty years ago.

To be fair, it looks like the same can be said for machine learning. Many software engineers I know are in the "machine learning space", but report that they are just operations support for data scientists, and don't actually get to learn about, let alone be involved with creating, the models they support.

If you are a software engineer, work in a software company, where engineering is the value proposition.

The academic elitism doesn’t end at PhD. A friend asked a Nobel prize winner their thoughts of different person who was recently awarded a Nobel prize in the same field. Their response “Ah, yes that was one of the lesser Nobels”