For those interested in his work, I really recommend viewing the recent documentary Jodorowsky's Dune, a failed film project Giger and several other proto-luminaries worked on (inspiring much of the iconic imagery in Alien, Star Wars, and Indiana Jones). Giger appears throughout the documentary. I believe it's still playing in the Bay Area.
Had a set theory professor who taught us that for the non-negative integers, m^n was just the number of unique mappings from a set of cardinality n to one of cardinality m. Ergo, for all sets A such that |A| = k, k^0 is just all mappings from Ø, which is necessarily the one with empty image and pre-image. So 0^0 = 1.
I've adopted a similar attitude as you here when it comes to past machine learning jobs, and discussion of detail. What ends up being your bright shiny line that you don't cross? I tend to just not talk about the specific feature engineering, being relatively upfront about such basic things as "I used a random forest".
Is "unlimited PTO" ever not window dressing for a more predatory policy? Anywhere I've worked with this policy had implemented it so that ever taking vacation was a negotiation with your boss (not something you could just comfortably declare), and it led to not being paid for any surplus vacation days (as none exist but an "infinite" amount) when you do finally leave said job because of burnout.
I bounced from one to a liberal arts college to Columbia. Also an anomaly, but it was a fantastic learning experience (and quite the redemption from terrible high school performance).
The four-weeks-vacation-but-feel-bad-about-that-and-occasionally-working-from-home thing does seem archaic and counterproductive for knowledge workers who are best when they're creative. I can understand how putting a fire out or having chance encounters with coworkers makes the full-time thing beneficial on the long tail, but the accumulated self-inflicted guilt and burn out go a long way toward that eventual decision of switching jobs (if just for the reprieve while ramping down and switching).
If Facebook/Google were in SF, I reckon many of its workers would still live in San Jose, Fremont and Oakland, due to affordability concerns and a lack of density in the city proper.
Pragmatic question: I'm a mathematician by training, and currently a machine learning engineer at a large Silicon Valley company. How does one in their late twenties jump into really working robotics? Machine learning was something I could watch lectures on, read papers about, work the math out on paper, then spend years in Python/C++ getting a feel for across various problem domains. Physical robots don't seem as accessible.
A friend was let go, as was an acquaintance who was hired in senior management of the group (Todd Beaupré). Investigating his LinkedIn page, he had circular recommendations with five others on his team, all who had finished working there at the same time (not including my friend).