25 September 2009

Some notes on job search

There are tons of "how to apply for academic jobs" write-ups out there; this is not one of them. It's been four years (egads!) since I began my job search and there are lots of things I think I did well and lots of things I wish I had done differently.

When I entered grad school, I was fairly sure that I eventually wanted a university job. During high school, my career goal was to be a high school math teacher. Then I went to college and realized that, no, I wanted to teach math to undergraduates. Then I was an advanced undergraduate and realized that I wanted to teach grads and do research. Teaching was always very important to me, though of course I fell in love with research later. It was unfortunate that it took so long for me to actually get involved in research, but my excuse was that I wasn't in CS, where REU-style positions are plentiful and relatively easy to come by (system development, anyone?).

However, the more time I spend in grad school, including an internship at MSR with Eric Brill (but during which I befriended many in the NLP group at MSR, a group that I still love), I realized that industry labs were a totally great place to go, too.

I ended up applying to basically everything under the sun, provided they had a non-zero number of faculty in either NLP or ML. I talked (mostly off the record) with a few people about post-doc positions (I heard later than simultaneously exploring post-docs and academic positions is not a good idea: hiring committees don't like to "reconsider" people; I don't know how true this is, but I heard it too late myself to make any decisions based on it), applied for some (okay, many) tenure-track positions, some research-track positions (okay, few) and to the big three industry labs. I wrote three cover letters, one more tailored to NLP, one more to ML and one more combined, three research statements (ditto) and one teaching statement. In retrospect, they were pretty reasonable, I think, though not fantastic. I don't think I did enough to make my future research plans not sound like "more of the same."

I suppose my biggest piece of advice for applying is (to the extent possible) find someone you know and trust at the institution and try to figure out exactly what they're looking for. Obviously you can't change who you are and the work you've done, but you definitely can sell it in slightly different ways. This is why I essentially had three application packages -- the material was the same, the focus was different. But, importantly, they were all true. The more this person trusts you, the more of the inside scoop they can give you. For instance, we had a robotics/ML position open (which, sadly, we had to close due to budget issues), but in talking to several ML people, they felt that they weren't sufficiently "robotics" enough; I think I was able to dissuade them of this opinion and we ended up getting a lot of excellent applicants before we shut down the slot.

Related, it's hard to sell yourself across two fields. At the time I graduated, I saw myself as basically straddling NLP and ML. This can be a hard sell to make. I feel in retrospect that you're often better off picking something and really selling that aspect. From the other side of the curtain, what often happens is that you need an advocate (or two) in the department to which you're applying. If you sell yourself as an X person, you can get faculty in X behind you; if you sell yourself as a Y person, you can get faculty in Y behind you. However, if you sell yourself as a mix, the X faculty might prefer a pure X and the Y faculty might prefer a pure Y. Of course, this isn't always true: Maryland is basically looking for a combined NLP/ML person this year to compliment their existing strengths. Of course, this doesn't always hold: this is something that you should try to find out from friends at the places to which you're applying.

For the application process itself, my experience here and what I've heard from most (but not all) universities is that interview decisions (who to call in) get made by a topic-specific hiring committee. This means that to get in the door, you have to appeal to the hiring committee, which is typically people in your area, if it's an area-specific call for applications. Typically your application will go to an admin, first, who will filter based on your cover letter to put you in the right basket (if there are multiple open slots) or the waste basket (for instance, if you don't have a PhD). It then goes to the hiring committee. Again, if you have a friend in the department, it's not a bad idea to let them know by email that you've applied after everything has been submitted (including letters) to make sure that you don't end up in the waste bin.

Once your application gets to the hiring committee, the hope is that they've already heard of you. But if they haven't, hopefully they've heard of at least one of your letter writers. When we get applications, I typically first sort by whether I've heard of the applicant, then by the number of letter writers they have that I've heard of, then loosely by the reputation of their university. And I make my way down the list, not always all the way to the bottom. (Okay, I've only done this once, and I think I got about 90% of the way through.)

In my experience, what we've looked for in applications is (a) a good research statement, including where you're going so as to distinguish yourself from your advisor, (b) a not-bad teaching statement (it's hard to get a job at a research university on a great teaching statement, but it's easy to lose an offer on a bad one... my feeling here is just to be concrete and not to pad it with BS -- if you don't have much to say, don't say much), (c) great letters, and (d) an impressive CV. You should expect that the hiring committee will read some of your papers before interviewing you. This means that if you have dozens, you should highlight somewhere (probably the research statement) what are they best ones that they should read. Otherwise they'll choose essentially randomly, and (depending on your publishing style) this could hurt. As always, put your best foot forward and make it easy for the hiring committee to find out what's so great about you.

Anyway, that's basically it. There's lots more at interview stage, but these are my feelings for application stage. I'd be interested to hear if my characterization of the hiring process is vastly different than at other universities; plus, if there are other openings that might be relevant to NLP/ML folks, I'm sure people would be very pleased to seem them in the comments section.

Good luck, all your graduating folks!

21 comments:

Anonymous said...

So, is University of Utah hiring in ML? :)

Fernando Pereira said...

In my experience, a bad -- wishy-washy, rambling -- research statement can hurt, but there's not much even the best one can do to add to letters and publication record. For cross-area candidates, a statement that clarifies the candidate's standing and goals on the two areas can be useful, at least to make sure the package is looked at by all the relevant hiring sub-comittees.

There's another ingredient that in practice is very important: "buzz." Around every hiring season, there's a lot of chatter about who the really "hot" candidates are. I've seen this most often in the theory community, where solutions for long-standing problems spread rapidly by word of mouth, and in graphics, where a stunning presentation at SIGGRAPH can get a lot of attention, but I've also seen it in other areas. There are all sorts of implicit biases in this process (old boys network, department pecking order, candidate characteristics that affect their perceived glamor), but anyone who is on the academic job market should be aware of it, and prepare appropriately. Doing a great job in conference talks by preparing very carefully, having your well-known advisor go around advertising your latest results in distinguished lectures at potential hiring institutions (but not too close to your own interview visit, of course; at least one year ahead), establishing good working relationships, even collaborations, with graduate students and postdocs at institutions where you may apply later, all of these things can help build up your social network and its ability to convey your message. I often said to my students (they must be sick and tired of it) that the most important thing in graduate school, besides doing significant research, is to build a strong research social network with many links besides those provided by your advisor.

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