Well, that's it. Hiring is f*cked. I'm just opened a contract role for a bioinformatics project. I know it's a wild market at the moment - each open role gets flooded with resumes, making it hard to separate qualified candidates from the noise. I'm convinced LLMs are partially to blame. So I devised a process to select qualified, committed candidates and avoid generic applications. Instead of resumes and cover letters, I created an intake questionnaire requiring bespoke effort to reduce cookie-cutter applications. The questions focused on specific scenarios from past experience, and therefore shouldn't be directly outsourced to chatbots. I also accidentally made the application link hard to access, but decided not to fix it. After all, computational biology often requires hacking and clever work-arounds. Finally, I included an honor code asking people not to use LLMs. Initially, I felt good about my approach. I had 20 applications after two days, and the candidates were differentiating themselves: A few didn't answer all questions, a few didn't have the right background, and several candidates provided well-written, topical, plausible answers. It was only after I read a few of these "high signal" applications in a row that the alarm bells started ringing: - Multiple candidates highlighted the same methodological paper - Many cited required skills in the exact same order - A high percentage reported identical troubleshooting scenarios involving differential gene expression studies with lab-derived batch effects The nail in the coffin was repeated phrases like "my initial hypothesis was a bug in [popular tool]" and "the spurious result disappeared." Clearly, candidates were using LLMs. Instead of learning about fitness for the role, I was learning how LLMs answer my "clever" questions. So...I failed. My approach was naive, perhaps even hypocritical. After all, I use LLMs for technical documentation myself. Still, it's really disappointing. I'm no closer to a hiring process that identifies qualified, motivated, and trustworthy candidates for remote work. Now what, y'all? | 109 comments on LinkedIn

In October 2024, I twote that “something is deeply wrong” with what we now call virtual cell models. A lot has happened since then: modelers are advancing new architectures and mining new sources of i...

And what might come next

Multi-part series exploring the tech and politics around short/long-read sequencing advances