I noticed that you've scaled the business quite quickly since inception a year ago, growing to around 100 employees.
I see a lot of advice saying to keep growth to only ~2x FTEs per year to preserve culture. How do you reconcile that with your hyper-scale financing and hiring trajectory?
It is definitely a risk. I think the first six months of the company we grew headcount probably too aggressively, and then the second six months of the company we did a lot of things to go back and fix some of the resulting cultural tensions (things like create company values collaboratively, figure out job roles better, etc). I'm pleased to say that we have made amazing progress there though (mostly due to the team, not me!).
Unlikely we grow beyond 2x headcount in the upcoming year.
Hong Kong is also an option. Relatively lenient on the work visa as long as you are skilled. Similar with a growing number of startups and big corps hiring lots of developers.
Shoot me an email (in my profile) if you need help / are strongly considering HK.
Part of it is the source code. It specifically scans for keywords such as SoMA and associates it with SF. Other countries / states / cities get no such special treatment.
Made me sad as my company in Hong Kong (and all others in Asia) doesn't show up.
Kites is a POI data company focused on Hong Kong and Southeast Asia. We aggregate data from individual merchants, scrapers, human sources, etc. and syndicate to many publishers.
We've been around for ~1.5 years and have 7 amazing people.
We're looking for a senior dev / architect. You want to deeply understand the customer and business model and drive the technology roadmap. You should be whip-smart technically and ideally have a solid theoretical CS background. Experience leading a technical team a big plus.
This position will especially appeal to hackers interested in gaining foreign experience. :) We'll help you relocate. Email in profile.
As an addition, if you're interested in working in the student space for LinkedIn, please contact me (email in profile). We're also looking for great engineers to tackle a very important market.
I would deeply appreciate advice from you or anyone who has thoughts.
I did a double major in CS and Business during my undergrad at CMU ('09) and focused very much on practical learning (read: programming/web apps) and corporate/startup endeavors. However, I was always drawn towards studying the relationship between minds and machines on my own time. Mostly triggered from Godel's theorem, reading GEB/AI books, and some obsessive impulse to learn about my own mind.
Now that I'm working my first job, this impulse is stronger than ever. I find myself reading papers/books on philosophy, anthropic mechanism, AI, etc. during what free time I have. I suspect that I should study a PhD in this subject, given this impulse doesn't seem to be going away.
However, I have absolutely no research experience and had little contact with professors during my undergrad. Would you advise I seriously pursue this intellectual interest as a PhD (versus during my free time)? If so, do you have any thoughts on how I should go about applying? Given that most applications require research recommendations, I was thinking of contacting professors of papers I admired, but am not sure how well that approach would work.
Thank you for reading! My email is in my profile if that works better.
A PhD is a formal license to do research and it marks the start (not the end) of a lifetime of research. You need such a license if you plan to work at a company with a rigid corporate ladder or in academia.
The only additional reason to get a PhD besides the license is an increased probability of being in contact with peers who you can collaborate with. People often undervalue this but empirically it's pretty clear what the benefits of having at least one research collaborator are.
If you actually do decide to go for a PhD, you're going to need at least one strong recommendation that speaks to your research ability if you want to get into a top program. Your undergrad institution and GPA put you in the running to be sure, but admissions committees are looking for evidence that you can perform research. Recommendations that say "this kid got an A in my class and is a good student" don't really have an impact on your application either way.
I don't want to give a "don't do it" answer, but I would say that it's difficult, so it'd only be worth trying to negotiate a PhD, academic publishing, where you fit into a discipline, etc., if you're really committed to a research career. It also depends on what exactly you'd want to study; a lot depends on finding a supportive advisor who would be willing to supervise the kind of thesis you want to work on. This depends not only on the style of work, but also the specific domain, e.g. you're going to get a totally different set of candidates if you're interested in, say, interactive entertainment (there's a sub-field of game AI, AI-for-narrative, etc.) or perhaps something to do with robotics (also its own subfield), or else something to do with human-computer interactions (something vaguely in HCI, CSCW, etc.).
"Big AI" isn't very much in favor currently, partly for good reasons and partly for bad reasons. There's a strong worry about being too unrigorous or philosophical or vague or even sci-fi. Academic AI probably overcorrects for a fear of being seen like crazy singularity-mongers, and there's also a legacy of having over-promised some big-AI stuff in the '50s and '60s. Most funding is also for more concrete technical projects, though there is a subset of people doing some funded research in the area of artificial creativity and creativity support (Margaret Boden and Gerhard Fischer are two entry points into that literature).
So, most research tends to be much narrower and investigate specific empirical or mathematical questions, like whether a particular reinforcement-learning algorithm converges, or how to improve an object-tracking algorithm, or something of that sort. Even in Cog Sci departments, the theses tend to be more specific, like doing an eye-tracking study that investigates some question about perception. To the extent the "bigger picture" stuff gets done at all, there's a feeling that it's a late-career thing people like Hofstadter can get away with, but it's harder to do as a PhD thesis.
Not sure that actually answered your question, but the short version is: it's hard to get in a position where you can study the kind of stuff discussed in GEB, but if you can think of more specific technical questions on the peripheries of your big-picture interests, it may be more doable.
I noticed that you've scaled the business quite quickly since inception a year ago, growing to around 100 employees.
I see a lot of advice saying to keep growth to only ~2x FTEs per year to preserve culture. How do you reconcile that with your hyper-scale financing and hiring trajectory?
Thanks, really appreciate your thoughts.