Deciding to go into the tech industry from academic linguistics is a decision I made after trying the academic job market out for a year, getting nothing out of it, realizing that I really didn’t want to move, and learning about some really interesting jobs outside of the academy. I was never really against the idea, so my “leaving academia” story doesn’t come with much emotional baggage.

A few months into my job, and I can already say that it feels like the right decision for me. This post is a mix of me telling my job-search story, the resources that helped me out a lot, and miscellaneous bits of information I learned throughout the process. I don’t claim to be an expert in getting hired but perhaps you’ll find something here that you can use. It’s a bit of a hodgepodge, and I may update it in the future.

Where I started

I started with reasonably strong skills in quantitative methods and data programming. This made my job search process easier. I could confidently list skills like experimental design, corpus development, data cleaning, and statistical analysis on my resume, and point to concrete examples like papers, GitHub repositories, or blog posts. I think this range and level of technical experience was a huge asset in my job search, but mostly because it opened up a wider variety of doors. It made me a competitive applicant for jobs with titles like analytical linguist, (quantitative) UX researcher, and similar.

What you can do now

Start gathering resources

Start by learning about what’s out there, and thinking about what you might be interested in doing. This includes figuring out what job titles to look for, which companies hire for linguist backgrounds, how much you can expect to be paid, what networking groups you can join, and who you can reach out to with questions. In no particular order, here’s a list of resources to get you started:

Once you’ve come up with a list of job titles, start exploring job ads for these titles. You can look on specific companies' websites, LinkedIn, Indeed, or any of the many job listing sites. These job ads will tell you what kinds of skills you should think about developing while you’re still in grad school.

Here are a bunch of job titles seen attached to linguists in the wild: Analytical Linguist, Applied Scientist, Conversation Designer, Corpus Linguist, Curriculum Designer, Data Curation Lead, Data Operations Linguist, Data Scientist, Language Data Analyst, Language Data Engineer, Language Data Researcher, Language Engineer, Learning Scientist, (Lead/Senior) Linguist, Linguist Manager, Linguistic Engineer, Linguistic Engineer, Natural Language Annotation Lead, Ontologist, Research Scientist, Speech Scientist, Strategy Linguist, Taxonimist, UX Researcher, Voice User Interface Designer… so many!!

Be find-able

If you don’t have a LinkedIn profile, go make one, and put some effort into setting it up for non-academic audiences. Take a look at how linguists in the networking groups I listed above present themselves. Tech/industry has a language, and you’ll benefit from learning to use it. You don’t have to like LinkedIn but it is what people use, and it will almost certainly help you. I got my first position because a recruiter reached out to me on LinkedIn and I responded promptly.

You may also want to have your own website if you don’t already. It can host your publications/presentations, collect links to all of your online presences, and depending on the field you want to go into – a portfolio. It doesn’t need to be fancy. Consider Twitter. Make sure your Google Scholar page is accurate. Basically, if someone is googling you, make their job easy, and make sure they find something good.

No matter what combination of profiles you use, make sure they all have the same (boring/professional) headshot. you don’t need to pay for this. I did my makeup one day and took like 200 selfies outside my house. A few of them were decent, I picked one, and you can see it on any of my web presences.

Start networking early

Networking is a skill, and I used to be really bad at it. But is also one of the most important things you can do. A few things you can do:

  • Interact with people in your desired profession on Twitter
  • Ask people for informational interviews via LinkedIn or Twitter
  • Go to networking events (virtual or in-person)
  • Find peers that want to do the same thing as you – in a few years you’ll be good professional connections for each other!
  • Join networking groups and sign up for mentorship if offered (or ask for it if it isn’t)
  • Find alumni from your program in industry jobs and talk to them
  • Be open to talking to people! I’m generally willing to chat with people on Twitter or schedule meetings (Calendly is amazing btw)

When you’re ready to apply

Getting a foot in the door can be challenging (and daunting). Building an online presence and networking can go a long way in greasing the wheels for your application process. Apply to both permanent and contractor jobs. Most people I know who went straight from Ph.D. to industry started with a contract job (including me). There’s a lot to dislike about the sheer volume of contracting roles at big tech companies, but it’s something you may need to accept. Think of it as a foot in the door, or a stepping stone on the way to something more permanent. And at least in tech, while it might be a fixed-term contract, it still probably pays better than most academic jobs.

When should you start?

Timing is a tough one. I started applying in July, interviewed in August and September, got an offer in September, and started working in late October. Some companies I interviewed at moved fast and others painfully slow. Think of my job search as a reasonably fast timeline. It really helped that I was job-hunting in a major tech hub – Seattle. Sometimes it takes 6 months or longer. It’s hard to predict, but you should know that there are annual hiring cycles. New positions tend to be minted at the beginning of the year, which results in a first-quarter (Jan-Mar) hiring frenzy. Hiring managers get jumpy about roles that are still open in September-October, so there’s another surge then. You probably won’t have as much luck in November or December. That said, people can leave at any time of year, and when they do, their position will likely become a job opening. So while there are cycles, they aren’t nearly as strict as the North American TT job market.

Resumes not CVs

1-2 pages, no exceptions. Write an executive summary. Quantify things as much as possible. Show not tell. Use the STAR method. Education goes last. And I’m sorry, but no one cares about your publications section (you can make it a bullet listing the number and where to find them). Exclude irrelevant things (no matter how much they mean to you). Tailor it to every job you apply to. And lastly, ask someone in industry to give you some resume feedback and share a successful one of theirs. Here’s the resume I used to apply to my UX Researcher position at Reality Labs Research in August 2021.

Job ads are your best source of information when you go to tailor the resume

  • Not all job ads correspond to specific jobs. Some are frustratingly vague. If you can, ask someone at the company if they can do some sleuthing for you.
  • Think of job ads as wish lists. If you meet 75% of the criteria, apply.
  • Fit matters a lot, and you won’t have all of the information you want. Again, ask someone!
  • Resumes may be screened by non-specialists, sometimes by non-humans. Make sure yours is machine-readable.
  • Incorporate job ad language into your resume as much as makes sense.
  • Look for company mission language on their website and echo it in your resume (e.g., Grammarly’s EAGER values or Amazon’s customer-obsessed verbiage)

Interviewing

Every company and type of role has a slight variation on the interview process. Often it’s a subset of the following. There will almost always be 1, 2, 4, and 5.

  1. Submit resume/application
    • Who: You! Or a recruiter or the person referring you internally.
    • Tips: See above. Cover letters aren’t very common, and I don’t have any good advice about whether or not to write them.
  2. Phone screen
    • Who: Often this is done by a recruiter or a member of the team (it can be the hiring manager but isn’t always)
    • What: A short call with mostly predictable questions (see tip).
    • Tips: if you have an internal referral, you are much more likely to get to this step. I used sites like The Muse to prep for this. If they ask about salary expectations, push back and say you want to hear a range first. In some states, like Colorado, they have to give it to you (Washington is joining the ranks soon!!).
  3. Take-home technical task
    • Who: You, in a specified window of time
    • What is it? A small set of problems intended to mimic your day-to-day. Think data annotation tasks, small design problems, what-if scenarios, explaining why a method fits a question, and similar. All of the take-home tasks I’ve seen so far have been fair and appropriate for the job.
    • Do I have to program??? Only if the job actually involves programming.
  4. Hiring manager interview
    • Who: You and the hiring manager
    • Tips: Again, I used The Muse, prepared answers for every question on the list, and was generally not caught off guard
  5. “On-site” interviews
    • Who: You and a panel of approximately 4-6 people with different roles at the company (i.e., the role you’re interviewing for, Product Managers, Designers, Engineers/Data Analysts, etc.)
    • What: One-on-one interviews with people in different roles, in some cases you’ll have to prepare a presentation. It may be in the office or over video calls.
    • Tips: ask the recruiter who you’ll be talking to and whether the interviews have a designated topic and/or whiteboarding/programming/analysis component.
  6. Team lead/director interview
    • Who: You and a senior person that needs to sign off on your hire
    • Tips: Idk, I’ve only done this once and it focused on team fit and a chance for me to ask lots of questions.

Some advice for all interview steps:

  • Speak their language! It’s not interdisciplinary, it’s cross-functional. You use slide decks and 1-pagers, KPIs, A/B testing, and think in terms of quarters and halves.
  • Answer questions concisely.
  • Dwell on the things they care about (sorry, it’s not linguistic theory!)
  • Always have a few questions for them ready to go. How is the team I’d be joining structured? What are some examples of projects you work on? How does your team support women?
  • Lots of companies have technical blogs, and they are a great place to look for info to bring up in your answers. Here’s an example for Meta UXR, Meta Technology & Innovation, Grammarly Engineering, Amazon Alexa, and Twitter.
  • Practice, either aloud to yourself or with someone else
  • I have been asked the palindrome question coding question (google it) several times now, and if you think the job you’re applying to will have any element or programming, be prepared to answer it or a similar level standard coding question. If you think you’ll have a full blown coding interview, you should get the book that everyone uses.
  • If you are feeling a lot as you leave academia, know that there are lots and lots of people in the same boat as you. Check out Twitter hashtags like #withaphd, #leavingacademia, #altac, #postac, #nonac.

Lastly, good luck!

There’s a huge element of chance in any given job you apply for. There’s also discrimination, even if every company’s latest DEI initiative wants you to believe otherwise. So don’t beat yourself up about not getting any one job. The good news is that there are more tech and industry jobs out there than there are academic ones.