BetterEngineer vs Turing: Why Human‑Led Nearshore Hiring Wins

You hit the gas on hiring, and all you got was more noise, more interviews, and more wrong people.

That’s the promise and the risk with an AI-powered hiring platform. The upside is obvious: AI‑driven matching, a huge global “talent cloud,” fast access to engineers from everywhere. On paper, it looks like the most efficient way to fill roles.

At BetterEngineer, we take almost the opposite route.

We’re not trying to be the biggest, most generic talent pool. We do have a deep network of senior LATAM engineers; we just don’t throw all of them at you. Our job is to be the most accurate for one specific thing: helping US tech companies fill roles with senior LATAM engineers who join the team, work your hours, and stay long enough to matter.

Volume Machine vs. Fit Machine

Think about what each model is really built to maximize. Turing is built to maximize volume and speed.

• AI and automation scan and score large numbers of engineers
• Matches are generated from a very big global pool
• The story is: “We’ll get you lots of good options very fast”

You get a lot of throughput. But you still carry much of the burden of deciding who will actually work on your team.

BetterEngineer is built to maximize fit and continuity:

• We talk with you about what’s gone wrong before, and what “great” really means on your team
• We pull from a focused network of senior LATAM engineers instead of “everyone, everywhere”
• We introduce a few people we genuinely believe will work, not dozens you need to filter

The story here is different: We’ll take on the hard part of saying ‘no’ so you see a small number of very good options.

If you’ve already discovered that “more” doesn’t equal “better,” that trade‑off starts to look appealing.

Global Talent Cloud vs. Senior LATAM Specialists

Turing’s pitch is the global talent cloud: a broad pool of engineers across many countries and backgrounds. We chose a narrower lane on purpose.

BetterEngineer is built around senior engineers in Latin America who already have experience working with US and international teams. We’re not hunting everywhere in the world. We’re focusing deeply on one region and one engineer profile. 

You feel that in practice:

• You’re not doing late‑night calls across extreme time zones
• You’re talking to people who’ve already navigated remote work with US‑based teams
• You don’t have to explain basic startup realities like shifting priorities and quick iterations

If what you really want is nearshore, senior engineers who feel close to your world, a global approach just isn’t necessary.

Why LATAM is a Better Nearshore Bet

Latin America isn’t a shortcut. It’s a strategic bet. Here’s what that gives you, very concretely:

• Time zones that line up: You can run standups, 1:1s, and working sessions in real time without turning your calendar into a graveyard of 7 a.m. and 11 p.m. calls.
• Engineers who’ve done this before: LATAM’s tech ecosystem is booming. Most of our engineers have already shipped products at U.S. startups, SaaS companies, fintechs, and other product‑driven teams. 
• More signal, less noise: Because we’re not reaching into every region, you’re not wading through endless profiles. The bar stays high and consistent. You don’t need a crowd; you need a few people you’d trust with your roadmap.
• Costs that make sense for both sides: Nearshoring to Latin America helps you extend your runway without relying on “cheap labor.” You lower total compensation costs, while engineers earn more than locally. Your runway benefits, and so do the people on your team.

We go into more detail on the advantages of nearshoring to Latin America here: The Tech Talent Hub You Need. 

Fast Matches vs. Stable Teams

AI can absolutely speed up matching. It can scan profiles, flag skills, and generate shortlists faster than any human team.

What it can’t do is own what happens six months after someone joins.

You’ve probably seen some version of this:

• The hire that looked perfect on paper never really leaned in.
• Someone great technically never clicked with how your team communicates.
• A few months later, they’re gone, and you’re back at square one explaining your product and codebase all over again.

That’s the hidden risk of “AI‑only” lists: algorithms can spot keywords and surface skills, but they can’t see mindset, ownership, or how someone will mesh with your team in real life. Candidates become data points; teams become collections of credentials instead of communities.

At BetterEngineer, we use technology to help, not to decide. We assume:

• Turnover is expensive, even if replacements are easy to find.
• The point of a senior hire is compounding value over time — context, judgment, and real ownership.
• You don’t get that if you’re rotating people in and out like tickets in a queue.

So yes, we care about speed. But we care more about whether that person is still on your team, shipping with you, a year from now.

Algorithms vs. Human Judgment

Turing is, at its core, a software platform. It’s powerful and automated and very good at what platforms do: process a lot of input and generate a lot of matches.

But there’s a hard limit to what software can see.

AI can help you:

• Sort thousands of profiles
• Check for baseline technical fluency
• Match keywords to a job description

What it can’t reliably do is:

• Read between the lines for authenticity or humility
• Understand the unwritten rules of your culture and team dynamics
• Spot future potential in a career path that doesn’t look “standard”
• Tell the difference between someone who ticks boxes and someone who will actually move the product forward.

That’s where we draw a line. BetterEngineer is, unapologetically, more human:

• You have real people on our side who know your story and your standards.
• We talk to candidates about real projects they’ve owned, not just the buzzwords on their CV.
• We look for communication clarity, ownership mindset, and curiosity — things no algorithm can really score well yet.
• We recognize that great engineers don’t all have linear careers, and we don’t filter them out because they don’t fit a neat pattern.

AI helps us move faster. Our team makes sure we’re moving in the right direction.

Under pressure, that difference matters. It’s the gap between “another tool that sent us a long list of profiles” and “a partner who actually understands what kind of engineers will thrive here and stay.”

BetterEngineer vs. Turing in One Quick View

Here’s how the models line up if you strip away the buzzwords:

Ready to Hire Differently?

If you’re tired of long lists, churn, and “AI‑matched” candidates who don’t really fit, try a different way. Let us know what you’re building and the kind of engineer you actually need; we’ll come back with a curated  list of senior LATAM engineers you can picture on your standup, not just in a dashboard.