
Hiring for AI Skills Beyond Prompting with Maksym Dziarmaga, Loop Earplugs
AI fluency is becoming part of the hiring bar, but assessing it is still a work in progress. Maksym Dziarmaga of Loop Earplugs joins Shannon to share how Loop evaluates AI capability throughout its hiring process. He explains why the company established "capable" as its baseline, how hiring teams assess candidates across different levels, and where subjectivity can make consistent evaluation difficult. Maksym shares how Loop encourages candidates to use AI in business cases while looking closely at the thinking behind their work. He also explains why curiosity can be a valuable signal and how Loop is working toward a more consistent approach to evaluating AI skills across hiring teams. Key Takeaways: Critical thinking matters: Look beyond whether candidates use AI and probe how they evaluate, edit, and challenge its output. Set a realistic baseline: Match AI expectations to the role instead of requiring advanced fluency when the work does not call for it. Calibrate your interviewers: Give hiring teams shared examples of different AI capability levels so individual experience does not set the bar. Treat assessment as data: Use interview results to identify gaps in your hiring process and understand where teams may need stronger AI skills. Timestamps (00:00) Introduction (00:07) Meet Maksym Dziarmaga (01:24) Why Loop assesses AI capability during hiring (03:14) Deciding how much AI fluency each role needs (05:25) Creating a practical scale for AI capability (07:34) Why critical thinking matters more than AI output (08:06) Applying AI case studies during hiring (13:31) How curiosity and critical thinking shows in a candidate (16:34) How AI stewards support teams at Loop (20:02) How Loop help employees upskill (22:46) What Loop learned from its AI interview data (25:28) Standardizing AI assessment as expectations evolve (27:23) Where to connect with Maksym















