Before landing a coveted AI researcher role at ChatGPT-maker OpenAI, one recent new hire says she went through an exhaustive job search that included 57 interviews across 11 companies.
Alisa Liu described the job hunt as “really challenging but also super rewarding” on X. She detailed her experience in a recent blog post, saying she wanted to share what she learned and “hopefully make the process a little less mysterious for the next person.”
At the end of her six-year PhD program in natural language processing at the University of Washington, Liu wrote that she applied for research scientist and technical staff roles in the AI industry.
Beyond the marathon of interviews, Liu said she participated in countless informal networking conversations in the lead-up to the job search, 46 recruiter calls, and 16 post-offer discussions.
“A huge part of my personal experience was managing all the emotions that come with being on the market,” wrote Liu, explaining that she found it “stressful navigating a huge decision space with incomplete information, where small choices with no right or wrong answers (like who to contact when) have an outsized impact.”
“Frankly, I was stressed, miserable, and not functioning in other parts of my life for several months,” she said. “Hopefully you find more joy, but if not, just know that you are not alone.”
Liu did not return a request for comment by Business Insider. An OpenAI spokesperson confirmed Liu’s hiring.
In her post, Liu offered a sort of playbook for navigating the AI hiring market. Among her recommendations: don’t burn yourself out using too many companies for practice interviews, be strategic about interview timing, and lean on your professional network to get your foot in the door.
“To state the obvious: try to do good work during the PhD, make friends, and collaborate a lot! To get that first interview, sometimes you need to have someone inside the company vouching for you,” she said.
While research experience may help AI job seekers secure an interview, Liu said that companies spend far more time evaluating technical skills and knowledge.
Liu said that her interviews ranged from machine learning coding questions, which she described as “by far the most common,” to technical discussions featuring “rapid-fire” questions on concepts such as 5D parallelism and positional encoding.
Other interviews included research discussions, behavioral questions, and even math assessments spanning everything from “fun logic puzzles to serious mathematical derivations with pen and paper,” she said.
“I would recommend brushing up on probability, linear algebra, and calculus,” she said.
Additionally, Liu wrote that there is “truly no better use of your time than studying for interviews” but that “nothing beats getting enough sleep” the night before one.
Preparing for interviews was only part of the process, she said, noting that receiving job offers came with its own set of challenges.
“The truth is that negotiating is hard. Nothing in our PhD prepared us for this, and unlike interviews, this part cannot be conquered by studying,” Liu wrote, adding, “Putting in energy here for a few weeks can, literally, be equivalent to years of work at the initial offer.”
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