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

NCBI's Gene Expression Omnibus (GEO) is a public archive and resource for gene expression data.

an AI researcher going back to school for immunology

DNA sequence deep learning models accurately predict epigenetic and transcriptional profiles, enabling analysis of gene regulation and genetic variant effects. While large-scale training models like E...

How bad publishing incentives hinder long-term thinking in computational biology research

Discovery has implications for ‘all cells and tissues’ and research bridged gap between scientists and non-scientists