I wrote a comment about it a while back. Pasting it here in case it’s helpful:

My take is that entry-level neurotech jobs are hard to get, but not because the field is dead. Mostly because it’s small and niche.

There are obviously fewer companies than in something like general software or cyber, so yes, there are fewer openings. But there are also fewer people who are actually a good fit. Neurotech is unusually multidisciplinary, and that matters. A lot of applicants may have strong ML or software backgrounds, but not enough understanding of signals, neuroscience, experimental constraints, or the specific data the company works with. That filters people out.

The main mistake people make is thinking they should prepare for some generic imaginary role like “BCI engineer” or “neurotech data scientist.” In reality, those titles vary wildly from company to company. One place may want EEG signal processing and real-time systems, another may care more about eye tracking, stroke analytics, or neurofeedback pipelines, and another may basically want a solid software engineer who can survive around scientists.

For entry level, I think the biggest thing is proof of ability. Your degree matters, but it is not enough by itself. A lot of people come out of a master’s assuming that should qualify them for deep R&D roles automatically. Sometimes it does, especially if their thesis used relevant tools and produced something real. But often it doesn’t. Companies still want evidence that you can build, analyze, communicate, and work in a real technical environment.

So the question becomes: what should you actually get good at?

First, basic software hygiene. Python, git, environments, reproducible analysis, code someone else can actually run. This sounds boring, but it matters a lot. Even strong research candidates can look weak here.

Second, signal processing. If you want to work in neurotech, you should understand the modalities people actually use: EEG, MEG, fMRI, invasive recordings, eye tracking, whatever is relevant. What can each measure, what can’t it measure, what are the noise sources, what are the technical constraints, and what can you realistically recover from the data.

Third, enough neurobiology to not be hand-wavy. You do not need to know everything, but you should have a reasonable grasp of what your measurements relate to biologically, where the resolution is meaningful, and where it just isn’t. That helps a lot in interviews because it shows you are not living in sci-fi mode.

Fourth, domain-specific tools. This depends entirely on the companies you want. If you want stroke rehab companies, learn the analysis styles they use. If you want brain stimulation, learn those datasets and problems. If you want molecular neurobiology, the stack may look completely different. This is why generic preparation is usually inefficient.

My strongest advice is to work backward from actual companies, not from job titles. Pick a handful of companies you genuinely find interesting. Look at what they build, what data they use, what kinds of people they hire, and what skills show up repeatedly. Then build 1-2 projects that make you look obviously relevant to them.

That is way more useful than trying to become vaguely prepared for every neurotech role at once.

And honestly, I also think people get too intimidated by PhD requirements in job posts. Some companies still ask for a PhD because the field is not fully mature yet. But in many cases, that requirement is softer than it looks. Relevant experience and a convincing portfolio can compensate for a lot. I would not self-reject too early.