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The most expensive engineering hire is the one who looks perfect on paper — and six months in, still can't move your roadmap.   Same stack, same years, passed every interview. One ends up leading architecture discussions. The other can't influence a single technical decision.   The résumé didn't lie. It just couldn't measure the things that actually decide whether an engineer succeeds on your team.   That's the part AI still can't solve on its own. Algorithms are great at similarity — the right languages, keywords, and background. But similarity only tells you who could do the work.   Matching tells you who's most likely to succeed doing it. Those aren't the same problem.   It's why we built MatchIQ as a sequence of evaluations, not a single score: Matched by AI, reviewed by recruiters, vetted by engineers. Each layer answers a different question. You can see it most clearly from the other side of the process — from the engineering leaders who've actually hired through it: Eric Dargelies, VP of Engineering of Tia.   So how confident are you that your next senior hire will actually succeed here — not just pass the interview? That's the question we set out to answer in the comments

AI is making candidate discovery faster. But faster discovery doesn't automatically lead to better hiring.   Engineering leaders aren't just hiring for technical skills. They're hiring for judgment, collaboration, context, and long-term impact.   That's why better matching requires more than algorithms.   So, what does better matching actually look like? The answer might be different from what you expect.   Read the full perspective in the comments.

Too often, engineering hiring starts with a job description. But the best engineering partnerships start with questions.   Before opening a new role, ask:   How does the team actually work? Where is delivery slowing down? What capability is missing? What outcome are we trying to improve? Those answers change everything. Because when the problem is clear, the hiring decision becomes clearer too.   The goal isn't simply to fill a role. It's to solve the right engineering problem.   Which of these questions do you think teams skip most often before hiring?

46% of new hires fail within their first 18 months. Most people see that as a hiring problem.   We see it differently.   Most engineering hiring conversations begin with: "We need engineers."   But the best engineering partnerships begin much earlier. Before talking about candidates, they ask:   - What is slowing the team down? - What capability is actually missing? - What does success look like after this hire?   Sometimes the answer is hiring. Sometimes the answer is understanding the engineering problem first.   That's why we believe great engineering hiring doesn't start with recruiting. It starts with engineering context.   What's the first conversation your team has before opening a new engineering role?

Before an engineer is hired, companies invest more than $22,000 in engineering time through interviews and technical evaluations.   That's why the process shouldn't start with résumés.   It should start with understanding the team.   Better engineering decisions begin with context.   Because hiring isn't about adding headcount.   It's about strengthening the engineering system.   How does your team approach engineering hiring today?   👇 Share your perspective below.

Hiring a software engineer takes 56 days on average. But that's not the metric engineering leaders should worry about. The real impact of a hiring decision isn't measured when the offer is accepted.   It's measured months later.   When architecture decisions become harder. When code reviews reveal how engineers think. When a new hire starts shaping the rest of the team. By then, the decision has already changed the engineering organization.   Most hiring processes ask: "Can we fill the role?"   Engineering leaders should be asking: "Will this hire make the team stronger?"   That's the idea behind our latest article: "Engineering Hiring Is an Engineering Decision". You can find the link in the first comment.   We're curious: What's the first sign that tells you a hiring decision was the right one?

A nearshore giant in our space is unwinding in public right now. I won't name them. The point isn't the name. The point is the pattern. Scale fast. Sign everyone. Promise the moon. Staff it with whoever's available. Let the quality average out somewhere south of what the logo suggested. It works — until it doesn't. Until the growth story meets the delivery reality and the gap shows up on a balance sheet. We've been pitched that playbook for years. "Just add more bodies." We never ran it. Nine years in, we still vet every engineer with engineers. We still say no to fits we can't deliver well. We've walked from deals that would've padded a quarter. That's not virtue. It's math. In services, your reputation is the product. You can borrow against it with hype for a while. The bill always comes due — and it comes due in public. The nearshore market is about to learn that lesson the expensive way. We'd rather have learned it slowly, on purpose, one honest "no" at a time.

Agentic AI job postings are up 280% this year. Entry-level tech hiring is down 25%. Same market. Same twelve months. Everyone wants to read this as "AI is killing engineering jobs." That's the lazy read. What's actually happening: the job description changed faster than the hiring process did. Companies aren't paying for someone who can write code anymore. AI writes code. They're paying for someone who can own a system — design it, ship it, catch the agent when it's confidently wrong. This week OpenAI pitched Codex "for every role." Apple put AI assistants in everyone's pocket. The tools are now everywhere. Which means tool access stopped being a differentiator. Judgment is the differentiator. We see it every week at Nexton. Clients don't ask "can this person code" anymore. They ask "can this person be trusted with a production system that's half-built by agents." Different question. Much harder to answer from a resume. That's the screen that matters now. Engineers vetting engineers was our bet 9 years ago. The market just made it the only bet that makes sense.

Four people just flew around the Moon. The first time in over half a century. Three Americans, one Canadian. The first non-American to ever travel beyond low Earth orbit. That Canadian is Jeremy Hansen, a farm kid from Ontario who became a fighter pilot, then an astronaut, then part of a crew that traveled farther from Earth than any human in history. I keep thinking about what it took to get there. Not the rocket. The people. Thousands of engineers. Years of preparation. Countless problems solved by teams that most of us will never know by name. The kind of talent that doesn't make headlines but makes the mission possible. That's what I think about every day: not the spotlight talent, the engine-room talent. The engineers who solve the problems that don't fit in a press release. Getting to the Moon isn't about four people in a capsule. Yes, they are the heroes. But it's about the thousands behind them who showed up, did their best, and made something extraordinary possible. That's what great engineering looks like. On any mission. At any scale. Just get up and do your best.

We just shipped something we’ve been wanting to build for a while. Every quarter, we were exporting a PDF with LATAM salary data and sending it out. It worked. But a PDF is a snapshot. It goes stale the moment you close the tab. So we rebuilt it from scratch, as a live product. The Nexton LATAM Tech Salary & Benefits Report is now an interactive dashboard, updated in real time. Not a document. A tool. What's inside: → Median annual salaries across several engineering roles (current market, USD). → Quarter-over-quarter compensation trends, so you can see where salaries are heading, not just where they are. → A direct comparison vs. US averages by role. The headline number that keeps coming up:  ~60% cost savings on senior engineering roles vs. US equivalents. But the more interesting insight is in the trend lines. LATAM engineering salaries are rising steadily. The market is maturing. The window for building a high-output remote team at a significant cost advantage is real, but it's a strategic decision, not an indefinite one. We built this for those who are tired of guessing.  Real data. Clear insights. Better decisions. Link in the comments.