I hired 100+ engineers between 2022–2025 and used a Quality Engineer on payroll to help me do it.
No, they weren’t a recruiter. He’s an engineer who became one of the most valuable parts of my hiring process.
He helped me:
• Vet candidates against the actual requirements of the role
• Validate whether candidates could really speak to what was on their resume
• Build interview rubrics that created consistency across hiring teams
• Bridge technical conversations when sales reps and hiring managers weren’t aligned
While many of my peers were relying on coding assessments as a proxy for quality, I was using an actual engineer as the baseline.
Which approach makes more sense?
I’ve always believed technical evaluation should map directly to the job. Not abstract problem solving. In practice, having technical talent embedded in the process led to better signal, better alignment, and fewer hiring mistakes.
I’ve always wondered why more staffing firms don’t operate this way. For a while, I assumed it was a scalability problem. So when I went internal and had to work with a staffing partner to help fill engineering roles @ Ovidius AI, my leadership team ended up wading through a lot of the same noise I had seen before. That is, until we met Molly and her recruiting team @ CloudEmployee.
What stood out was simple: they had built that same philosophy I value into their process.
They don’t rely solely on recruiters, surface-level screening, or algorithmic assessments. They involve engineers in validating technical capability and real-world experience before candidates ever reach me.
So I’m not spending my time decoding resumes or trying to infer whether someone’s experience actually maps to their portfolio. The filtering and vetting has already been done.
Since I haven’t come across many staffing organizations that have operationalized this approach at scale, I sat down with Molly to understand how it actually works in practice.
Honestly, I’ll just let the Q&A below speak for itself.
The Conversation:
1. Tell me how your technical process works?
Our technical vetting is run by engineers, overseen by our CTO (Anto) as opposed to recruiters. That distinction matters more than it sounds. Recruiters can screen for keywords. They can’t tell you whether a candidate actually understands why they wrote the code they wrote, or how they’d hold up when a requirement changes mid-build. We can, because the people running our interviews have been in the room where those problems happen.
The process combines a pair programming sessions and behavioral evaluation which includes workstyle, motivational drivers, and how someone responds in real world situations. We’re not just testing someones memory; we’re testing how they think and how they’ll function as part of a client’s existing team.
One of the things that shapes how we interview is the data we’ve collected from over a thousand live technical transcripts. Patterns that look like competence on a resume (fluent use of terminology, impressive portfolio projects) don’t always hold up when you ask someone to modify their own code live, or to explain why they made a specific implementation decision. We look for that depth. Things like: can they reason from first principles when memory fails? Do they adapt when you give them a nudge, or do they dig in? How fast do they reorient when the problem shifts?
The output of all that is a shortlist of two candidates, custom-matched for the client’s stack, seniority level, and team culture.
2. Are the candidates prepped beforehand? Do they know what to expect?
Our recruiters provide prep before the candidates enter our technical interview process so they know what to expect. And just as important, before any candidate interviews with our client, we make sure they have a genuine understanding of what the client is building, current gaps/what’s blocking them, and what success in the role actually looks like. We pull that context directly from discovery call and technical call transcripts, so it’s not a generic J/D briefing, it’s specific to that client’s situation.
We want clients to see who the candidate actually is: because that’s the only way the placement works long-term. So the prep is about clarity and context, not performance optimization.
**This resonated with me the most. I hear this often from candidates: preparing for one conversation and walking into another. It usually comes down to a lack of context upfront, which impacts both the candidate experience and the quality of the interview. That matters to me because candidates are also interacting with my company’s brand. This is where Molly’s team stands out.**
3. Why do this instead of LeetCode or Hackerrank-style assessments?
LeetCode tells you whether someone can solve an algorithm problem under pressure with no context. That’s a narrow skill, and it selects for a narrow type of person.
The engineers who ace those assessments aren’t always the ones who do well when a production system breaks on a Sunday, or when the brief changes halfway through a sprint.
We’ve built our process around what actually predicts on-the-job performance, which we know from tracking outcomes across thousands of placements over eleven years.
Some of the most diagnostic signals have nothing to do with raw problem solving speed. They’re things like: how quickly someone incorporates feedback during the interview itself, whether they can explain tradeoffs in their own implementation, and whether they take ownership of past decisions or deflect to the team.
Automated assessments also miss behavioral and cultural signals. How someone works under ambiguity, how they communicate when they’re uncertain, and how they operate inside a team. For us, those things matter more than algorithm speed.
**I could not agree more and that’s a large part of what attracted me to Molly’s team in the first place. I hope to see other companies follow suit over the next year**
4. What’s the interview-to-offer ratio once someone passes vetting?
When a candidate clears our vetting process, we present two matched candidates to the client. Our interview-to-hire ratio is roughly 2:1 because one of those two typically gets hired. And that’s intentional. By the time a candidate reaches this stage, the technical bar has already been cleared. The client interview is focused on fit, communication, and working style, not re-litigating technical competence. We’ve already done that work, so a 30-60 minute conversation is usually enough to decide fit.
5. How do you source candidates and how many applications do you receive per role?
Zero. There’s no job board listing. No “apply here.” No inbound funnel. We have sourcing teams across Latin America, Eastern Europe, and the Philippines. Their job is to proactively identify engineers who match a client’s stack, seniority, and working style, and reach out directly. We do not use AI to source or filter through candidates.
Most of the engineers we place are not actively job hunting when we find them. If they’re interested, they move into our vetting process. We’ve built an outbound sourcing model designed to minimize noise at the front end and create signal before candidates ever enter the pipeline. In this market especially, we’ve doubled down on approaches that avoid high-volume inbound entirely.
Final thought
What stood out to me in this conversation wasn’t just the technical eval process, but how intentional every part of it is.
Coming from staffing, you hear a lot of “we also vet technically” claims. This is different. It’s a system built on one belief: engineers are better at evaluating engineers than recruiters.
Appreciate Molly for taking the time to walk through their process in detail and for being open about how it actually works behind the scenes! If you’re hiring engineering talent, they’re worth knowing.