I am curious why you're transitioning off from Datadog?
A monitoring solution based on Grafana and Prometheus tends to have a high initial setup cost plus on-going maintenance since it's self-hosted and lacks some of the features that Datadog offers out of the box (alert customization for instance).
I have both worked with and interviewed people in their 40s when I worked at a FAANG. Despite what's advertised by HR, culture tends to be org/team-dependent at bigger tech companies. I believe that you should be more concerned about culture fit which is something you can assess during the interview. If you decide to interview for an individual contributor position, I recommend that you brush up on algorithms. You can either target a company on LeetCode or go through a generic list of problems (here is the one I use https://theinterviewlist.com)
In addition to the resources others have suggested here I recommend going through this list of problems that I put together for the coding portion of FAANG interviews: https://theinterviewlist.com
The salaries shown on H1BPay are the salaries that the company promises to pay or is currently paying the H-1B applicant.
There are 3 common cases of when the paperwork is filed. First, foreign students who graduated from American universities in certain fields can work for a certain period of time after which they can transition to H-1B. So the salary is pretty much what they are paid when the application was filed which is somewhere between 1 and 27 months. Second, people hired from overseas or within the US who aren't new graduates and don't have a current H-1B visa. Their salary would be what the company offered to get the talent. The third case is when you transfer you H-1B visa where an application is not required so there is no change to the salary in the application.
Looking at glassdoor.com and my personal experience, salaries are much lower than what people are claiming on HN
A major problem with Glassdoor salaries is the absence of a date range. The salaries they show is the average over the period of time starting when they started collecting salaries. So yes the salaries on Glassdoor tend to be lower than the current average.
A lot (too many) people on HN are claiming $200k base salary per year as being totally normal.
I agree. It is pretty high. My own reference for base salaries is my own website where I collect H-1B salaries. We also have an option to only show the salary distribution starting on a given year. For instance, for a software engineer at FB, the average base salary is $149k if you only include salaries reported after 2015 [1]. At Netflix however, Sr. Software Engineers make $200k on average[2].
There are relatively easy and cost effective ways to filter candidates from the supposedly less-than-stellar schools. Personal projects, open source projects contributions and programming contests are a good way to easily gauge candidates for a software engineering position.
It is surprising that you thought of this as a "cool AI job offer". I have two remarks here. First, the email sent to the class is barely an invitation to send resumes. Something many programmers/CS Students with online presence experience on a regular basis. Probably not from a Stanford Professor but at least from major companies recruiters. It would be interesting to know how many will actually make it through the screening, phone/on-site interviews and get a job offer.
Second, I registered for the Machine Learning course (I am not sure if the same applies to the AI course) and I compared it with the actual ML course at Stanford (CS229) (I mainly looked at Youtube videos of Andrew[1] as well as Assignments/Midterm[2]). The latter is by far more advanced and theoretical. The assignments tend to test more than basic comprehension of the material presented in the lectures, which is exactly what the online course reviews tend to evaluate. They require strong mathematical knowledge and obviously a minimum level of creativity/intelligence.
CHI is a huge conference. More than 1000 papers were submitted last year. A designated committee chooses 1% of the submitted papers to receive the best papers awards which explains the number of awarded papers.