Hiring software engineers is one of the hardest problems in business. I've spent years on both sides of it: as a B2B sales leader selling technical services, and as the founder of a fully remote software consultancy whose reputation depended entirely on the quality of the engineers we placed.
At some point, we got tired of how broken the process felt and decided to build our own solution: an AI-powered video interviewing platform for technical hiring. Building it taught me more about hiring than any number of interviews I'd conducted myself.
Here's what I learned.
The real problem isn't a shortage of candidates. It's a shortage of signal.
When people talk about technical hiring, they usually talk about the talent shortage. In my experience, the bigger problem is noise. Resumes look alike. Keywords get stuffed. Credentials say little about how someone actually thinks through a problem or explains a decision to a teammate.
Meanwhile, the traditional fix, more live interviews, is expensive. Every hour a senior engineer spends on a screening call is an hour they're not building. Scheduling across time zones adds days. And because different interviewers ask different questions, two candidates for the same role often have very different experiences.
So the question we asked wasn't "How do we use AI?" It was "How do we get better signal, earlier, without burning our best engineers' time?"
Where AI genuinely helps
Consistency. A structured video interview asks every candidate for a role the same core questions in the same format. That alone removes a lot of randomness. Candidates are compared on the same basis instead of on which interviewer they happened to get.
Asynchronous flexibility. Candidates can record their answers when it suits them. For a remote company hiring across many locations and time zones, that removes one of the biggest sources of delay. It's also better for candidates who are currently employed and can't take calls during work hours.
Summarizing and organizing. AI is good at turning long recorded answers into structured, reviewable information, so a hiring manager can focus their attention where it matters instead of scrubbing through hours of video.
Prioritizing human time. The biggest win isn't replacing the interviewer. It's making sure the interviewer's time goes to the candidates most likely to be a strong fit, and to the conversations that really need a human.
Where AI should not have the final say
This is the part I feel most strongly about. AI should support hiring decisions, not make them.
Technical roles involve judgment, collaboration and context that no model fully captures. Someone may give an unconventional answer that's actually brilliant. Someone else may be nervous on camera but exceptional on a team. And any automated system can carry over biases from the data or criteria it was built on.
That's why we designed our process around a few non-negotiable principles:
- Humans make the hiring decision. AI organizes and highlights; people evaluate and decide.
- Criteria come first. Before any interview, define what "good" looks like for the role. AI can only be as fair as the criteria you give it.
- Transparency with candidates. Candidates should know how the interview works and what's being assessed.
- Regular review. Keep checking outcomes. If the process seems to be filtering out good people, fix it.
- Respect the candidate's time. An async interview should be focused and reasonable in length, not an endless take-home in disguise.
What changed for us
The biggest shift wasn't speed, although things did move faster. It was confidence. When every candidate has gone through the same structured process, conversations about who to move forward become far more grounded. Instead of "I had a good feeling about them," we could discuss specific answers to specific questions.
It also changed how our engineers felt about hiring. Being pulled into fewer low-value screening calls made them more engaged in the interviews that mattered.
And candidates noticed. A clear, consistent and flexible process tells a candidate that you take their time seriously, which matters a lot when the best engineers have options.
Advice for teams considering AI in technical hiring
If you're thinking about bringing AI into your hiring process, here's my practical advice:
- Start with the bottleneck, not the tool. Is your problem screening volume, scheduling delays or inconsistent evaluation? Pick the problem first.
- Structure before automation. If your interview process isn't structured, AI will just automate the inconsistency. Fix the questions and scoring criteria first.
- Keep a human in the loop at every decision point. Especially for rejections.
- Measure what happens after the hire. The point of better hiring isn't a faster pipeline. It's better teams. Look at how hires perform and stay over time.
- Be honest with candidates. Explain the process. Most people appreciate clarity.
The bottom line
AI won't fix a broken hiring process on its own. But when you use it to add structure, consistency and flexibility, and leave judgment to people, it can make technical hiring faster, fairer and more humane.
That's the standard I held my team to at the remote consultancy I founded, and I think it's the standard the whole industry should aim for.
Ashkan Rajaee is the founder of TDZ Pro (https://tdzpro.com). As a partner he helped grow a software consulting company from about $6M to $60M in under three years.
Related: From $6M to $60M in Under Three Years: What Hypergrowth Taught Me https://www.linkedin.com/pulse/from-6m-60m-under-three-years-what-hypergrowth-taught-ashkan-rajaee-kfwkc/
Top comments (0)