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We’d like to introduce Brian Jenney, a senior software engineer and owner of Parsity, an online education platform that helps people break into AI and modern software roles through hands-on training. Brian will be sharing his advice on engineering careers with you in the coming weeks of Career Alert.
Here’s a note from Brian:
“12 years ago, I learned to code at the age of 30. Since then I’ve led engineering teams, worked at organizations ranging from five-person startups to Fortune 500 companies, and taught hundreds of others who want to break into tech. I write for engineers who want practical ways to get better at what they do and advance in their careers. I hope you find what I write helpful.”
Last year, I was conducting interviews for an AI startup position. We allowed unlimited AI usage during the technical challenge round. Candidates could use Cursor, Claude Code, ChatGPT, or any assistant they normally worked with. We wanted to see how they used modern tools.
During one interview, we asked a candidate a simple question: “Can you explain what the first line of your solution is doing?”
Silence.
After a long pause, he admitted he had no idea. His solution was correct. The code worked. But he couldn’t explain how or why. This wasn’t an isolated incident. Around 20 percent of the candidates we interviewed were unable to explain how their solutions worked, only that they did.
When AI Makes Interviews Harder
A few months earlier, I was on the other side of the table at this same company. During a live interview, I instinctively switched from my AI-enabled code editor to my regular one. The CTO stopped me.
“Just use whatever you normally would. We want to see how you work with AI.”
I thought the interview would be easy. But I was wrong.
Instead of only evaluating correctness, the interviewer focused on my decision-making process:
Why did I accept certain suggestions?Why did I reject others?How did I decide when AI helped versus when it created more work?
I wasn’t just solving a problem in front of strangers. I was explaining my judgment and defending my decisions in real time, and AI created more surface area for judgment. Counterintuitively, the interview was harder.
The Shift in Interview Evaluation
Most engineers now use AI tools in some form, whether they write code, analyze data, design systems, or automate workflows. AI can generate output quickly, but it can’t explain intent, constraints, or tradeoffs.
More importantly, it can’t take responsibility when something breaks.
As a result, major companies and startups alike are now adapting to this reality by shifting to interviews with AI. Meta, Rippling, and Google, for instance, have all begun allowing candidates to use AI assistants in technical sessions. And the goal has evolved: interviewers want to understand how you evaluate, modify, and trust AI-generated answers.
So, how can you succeed in these interviews?
What Actually Matters in AI-Enabled Interviews
Refusing to use AI out of principle doesn’t help. Some candidates avoid AI to prove they can think independently. This can backfire. If the organization uses AI internally—and most do—then refusing to use it signals rigidity, not strength.
Silence is a red flag. Interviews aren’t natural working environments. We don’t usually think aloud when deep in a complex problem, but silence can raise concerns. If you’re using AI, explain what you’re doing and why:
“I’m using AI to sketch an approach, then validating assumptions.”“This suggestion works, but it ignores a constraint we care about.”“I’ll accept this part, but I want to simplify it.”
Your decision-making process is what separates effective engineers from prompt jockeys.
Treat AI output as a first draft. Blind acceptance is the fastest way to fail. Strong candidates immediately evaluate the output: Does this meet the requirements? Is it unnecessarily complex? Would I stand behind this in production?
Small changes like renaming variables, removing abstractions, or tightening logic signal ownership and critical thinking.
Optimize for trust, not completion. Most AI tools can complete a coding challenge faster than any human. Interviews that allow AI are testing something different. They’re answering: “Would I trust this person to make good decisions when things get messy?”
Adapting to a Shifting Landscape
Interviews are changing faster than most candidates realize. Here’s how to prepare:
Start using AI tools daily. If you’re not already working with Cursor, Claude Code, ChatGPT, or CoPilot, start now. Build muscle memory for prompting, evaluating output, and catching errors.
Develop your rejection instincts. The skill isn’t using AI. It’s knowing when AI output is wrong, incomplete, or unnecessarily complex. Practice spotting these issues and learning known pitfalls.
Your next interview might test these skills. The candidates who’ve been practicing will have a clear advantage.
—Brian
Around this time last year, CEOs like Sam Altman promised that 2025 would be the year AI agents would join the workforce as your own personal assistant. But in hindsight, did that really happen? It depends on who you ask. Some programmers and software engineers have embraced agents like Cursor and Claude Code in their daily work. But others are still wary of the risks these tools bring, such as a lack of accountability.
Read more here.
In the United States, starting salaries for students graduating this spring are expected to increase, according to the latest data from the National Association of Colleges and Employers. Computer science and engineering majors are expected to be the highest paying graduates, with a 6.9 percent and 3.1 percent salary increase from last year, respectively. The full report breaks down salary projections by academic major, degree level, industry, and geographic region.
Read more here.
If given the opportunity, are international projects worth taking on? As part of a career advice series by IEEE Spectrum’s sister publication, The Institute, the chief engineer for Honeywell lays out the advantages of working with teams from around the world. Participating in global product development, the author says, could lead to both personal and professional enrichment. Read more here.
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