[pchwalek]

Im in a PhD program at MIT where I mainly build embedded systems and its not something I would recommend unless you are passionate about it. Hardware is "hard" and frustrating to debug since you are navigating both the hardware, embedded software, and sometimes the external software the device talks to. Many of my peers just do software-focused projects and can iterate much quicker. On the flip side, showing people physical artifacts that I've built is rewarding. Having the skillset to build a physical product from the ground up is nice and I would argue there are less people around that can do that. I get at least one person a week that comes to me to help them debug a hardware project since they mainly learned through Arduino sketches but when shit hits the fan, its a daunting task to debug beyond the sketch.

The other thing to note is if trying to spin out, finding VCs to fund a hardware startup is also tricky since the time horizon is much longer than a software startup. That being said, I've worked at a hardware startup before and if you have a compelling enough story, you can raise money without needing an initial prototype. In fact, I would recommend this since I also know of a lot of engineers who've spent a lot of time building something first and then there is no market/funding for it.

Now getting back to me not recommending this route, especially on the PhD level. Stable jobs for this line of work are hard to come by and are often underpaid for reasons I don't entirely understand besides that pure software deployments can reach larger audiences in a shorter amount of time which equals more ROI. The space is a bit fragmented and the level of skill varies. Many undergrads aren't really taught embedded-c either and often gravitate to higher level languages which are more domain flexible. Unless you land a job at a tech company building rapid internal prototypes which pays fairly well, the more common route I see in this space is for highly skilled people working contract jobs which can be too unstable for some. If you really wanted to go this route, I would recommend coupling embedded engineering with another domain (e.g., edge machine learning, technology applied for conservation/ecology). I did this about 2 years ago in my PhD and I can say that it has brought me significantly more attention and interest from funders and potential employers.