Consider working on genomics

The fact they renamed human genes because they were importing it in Excel in a way it thought they were dates and was changing them says a lot.

Can you really trust the scientific results if they depend on software made by people who don't care about code quality?

Molecular Biology of the Cell got me extremely excited about genetics and bioinformatics, highly, highly recommend this book to any software person I meet who is interested in biology.

As to the work environment, it seems to be extremely varied depending on the lab and team your on. I came from a number of years doing web development in marketing and finance before joining an R1 university research lab, and in many ways the day-to-day is quite similar in both fields. You are not the 'go-to' person for most things, but with that said, even as an individual contributor I feel my voice is heard on technical decisions where appropriate. As for pay, it's the biggest aspect that will make me leave at some point. If you do not have a PhD, or even a degree in my case, you can't expect to get paid a lot. As to the speculation on the satisfaction of the work, it is indeed deeply satisfying!

I got to have a conversation with one of the hero donors that gave a kidney biopsy after a life-saving transplant. It's hard to overstate just how impactful your work feels when talking to someone like that. Even as a small cog in the larger machine (our lab is around 50 strong with many people being at the top of their sub-fields), the end results of the effort will be massive improvements in individuals quality of life, this alone makes it quite easy to get out of bed in the morning.

When considering software roles in science organizations, forget assumptions you might make about a typical tech job, joining a bunch of other software and hardware people -- or you'll risk accidentally ending up on the other side of a distorted status system (not the side that normally pampers techbros).

You need to feel out the particular person you'll be reporting to on how well they personally respect and understand the role, and also whether they'll have clout/funding and have your back if the org turns out to be rough (think AMZN). And also try to feel out respect within the organization, and some of the people/teams with whom you'll be collaborating.

You also need to check compensation, so you don't wind up a low-paid person who later discovers they're competing for local house offers with others in the org who are getting big-bucks TC (plus consulting on the side).

You also probably have to be OK with never being the star (like you hypothetically could someday be in a software company). Supporting actors should still get respect and get paid.

Find the right science situation, and you might have much more positive impact on the world than you could have in a software company, while also being happy and comfortable.

Some more quick of-the-cuff comments about this (sorry for run-ons, but I need to get back to my weekend)...

* RESPECT -- Whether or not the organization is university-affiliated, a lot of the researchers and administrators might have only worked in academia-like environments before. Academia is very hierarchical, software engineering might be considered commodity technician or support staff, and the high-status people almost certainly don't understand your discipline, though they might think they do. (They often think software is relatively easy grunt work, and that software people just have oversized egos, which has some truth to it, but not that much.)

(Some real-life instances of this I've heard of include: someone with no understanding overriding software engineering technical decisions, because a colleague from their academic caste made an offhand comment, and they assume an academic who hasn't even looked at the system knows more than an experienced practitioner developing it; not wanting to include people who made key software contributions as coauthor on a paper for a software system, but making sure professors who had near-zero involvement were included; scientists openly speaking of the software people as having commodity interchangeable skillsets, in way they'd never speak about peers in their domain; getting an unsalvageable monstrosity of pasted-together incompatible frameworks and Stack Overflow posts done by a summer intern, dumped on software engineer to "clean up" or "extend", and being unable to convince that this is orders of magnitude harder to fix than to just make a viable system in the same time the intern took; in an academic environment, a grad student being higher status than key software people, and bossing them around with bad decisions, while treating their own obligations like homework they were trying to sneak past a grader rather than as a system that has to actually work.)

* COMPENSATION -- Related to the above. If you're very experienced and marketable in tech, and would be making key enabling contributions, are you getting paid like it?

(The most recent life sciences software engineering opportunity I talked with, with a high-profile organization, they needed FAANG-like Staff/Principal experience in multiple areas, all-in-one person, for key bespoke computational infrastructure on which a lot was riding. When we got to salary, it was capped at less than a new grads were getting offered elsewhere, and despite being in a top HCOLA city. The recruiter half-heartedly argued about it being for the science, etc. I said, if they're thinking of this as an academic non-profit, that would be OK, so long as everyone there is making this level of money. But that wasn't the case: the science domain people were considered the valuable assets, making good money, and software was seen as more a commodity support skill by whomever set the pay grade. Maybe within a decade that will agree with the market, everyone will decide that someone who can learn organic chemistry should get paid more than someone who doesn't seem to do much more than fingerpaint in a Web framework builder and type nonsense in Jira, :) and maybe then most software people will be thankful for any job at all, but not yet.)

(I did actually look at a science company with a strong software tech company influence. But, though they claimed to be rethinking how the tech company did things, they seemed to carbon-copy the single most obvious bad side of that company. Talking with colleagues after I withdrew my application, the gossip was that they were getting lots of software people who'd burnt out on the tech company. So I guess maybe the rethinking was on what had been bothering those people, who were already at the tech company, and so who weren't entirely representative of the talent pool that included people for whom the tech company had showstoppers.)

A: The goal of my current project is to identify novel pandemics, even if they're caused by something we've never seen before, most likely by looking at growth patterns. At a broader scale, I'm trying to learn enough about working in this field that I'll be able to contribute on whatever future projects seem most appropriately important to me.

B: unlike my previous work, I'm back to being an individual contributor. Very few meetings, mostly coding and analysis. Current thing is trying to understand what drives per-sample variability in wastewater sequencing data.

C. Our group is currently philanthropically funded, and is focused on determining whether/how this is possible/practical.

D. I'm mostly working in (bio)python, with a few bits written in C and gluing things together with bash.

E. I was most recently working on (a) JS infrastructure to fetch and render ads and (b) working with browsers on platform features that would improve privacy, security, and efficiency.

Edico developed FPGA-based processing solution for common bioinformatics processing tasks (e.g. dna/rna mapping, variant calling) and the company was bought by Illumina.

The product (Dragen) has been around for a few years and now will be integrated in the new generation of sequencers. Extremely impressive technology and a better fit for the niche compared to GPU-based solutions I have seen. More downstream processing and analytics is sometimes closer to traditional ML and naturally there are lots of GPU-based algos.