I'd like to get a better sense of what you're looking for in ML and what's been growing your interests. Are there any specific areas of ML that you'd particularly like to work in? What kinds of problems are interesting when it comes to ML? e.g. infrastructure for large-scale training, hyperparameter optimization, building new architectures, explainability/interpretability, applying ML knowledge to find new business problems to solve (not exhaustive by any means). 

ML is growing rapidly, and MLE is starting to be as nondescript as general SWE in terms of required skills, day-to-day, etc.

Your backend experience will absolutely be helpful, a lot of the most difficult problems in the space are about execution rather than novel ideas.

Senior Engineer at Series B Startup

Henry Prior

Seconding Alex's statement that 50 is large for a seed stage. I'd go as far as to say that it's excessive in almost all cases. My first concern before figuring out if it's even a good fit would be primarily financial:

- How much money was raised in the seed round? How long has it been since the raise?
- What is the burn rate? What is the current runway?
- How has hiring directly translated into solving business needs and forming the initial product? How would my joining drive this further?

With no other context I would expect a 50 person seed stage startup to lay people off due to excessive hiring in this climate. Your questioning should be thorough enough to fully rule that out. If the company is actually solid they shouldn't mind you asking these questions, and should understand your concerns. If they're cagey and take offense to these kinds of questions I would take it as indication that they know their decision making has been poor and aren't being honest with you or possibly even themselves.

If you get past the above angle of questioning I recommend figuring out a *specific* project that seems to be a good fit between the company needs and your own skills and personal goals. One of the main benefits of a startup is expanded scope and impact, but there's still a constraint on business needs. I touch on this in depth in the Taro session I did with Alex over the summer: <https://www.jointaro.com/lesson/8hT0g5peO8fwDZmvvIFZ/session-6-maximizing-swe-growth-ownership-and-specialization-at-startups/>

To @Rahul's point you may be able to crowdsource this by looking at Blind posts; a simple approach would be searching to find people considering leaving a company and people considering joining a company. Specifically in the realm of considering the financial aspect of career decisions, I expect that people who are posting on Blind are spending a disproportionate amount of time figuring out the best choices.