We place calibrated, bilingual AI evaluators across Latin America who grade model and agent output against your rubric: RLHF preference data, golden sets, multi-turn and tool-use trajectories, red-teaming. Every evaluator clears a calibration test before you meet them.
You can’t fine-tune, RLHF, or ship an agent safely without calibrated humans grading output against a clear rubric. LLM-as-judge drifts without a human anchor.
Stop burning expensive ML engineer hours on labeling and grading. Put dedicated evaluators on it, inside your tools.
One calibrated evaluator or a pod of fifty. Inter-rater agreement measured, reported and held throughout.
AI Evaluators
LatAm gives you something most eval pools can’t: genuinely bilingual and trilingual evaluators who can grade Spanish and Portuguese output as fluently as English, in your timezone, so calibration sessions happen live, not overnight.
3-5 people who fit the exact role, not a résumé dump.
Every evaluator clears a blind calibration test against a gold-standard rubric before you meet them.
Not a fit? We replace at no cost.
Operators who’ve made this hire, not account managers.
We start with a 30-minute call. You tell me the role, the stack, and what “great” looks like. No charge, no obligation.
We screen against the role-specific bar and send 3-5 people in days, not a résumé dump.
Meet your favorites. We coordinate everything. Most clients hire within ~3 weeks of the first call.
We handle contracts and payroll via Talent Management (COR). Not a fit in 90 days? We replace at no cost.
AI evaluation is a new discipline. The pitfalls are different from software engineering hiring. Four we see most.
Model-graded evals are cheap and they drift with the model doing the grading. A human-graded gold set is the anchor that tells you whether the judge, and the product, is actually getting better. Use both; calibrate the judge against the humans.
Two evaluators looking at the same output can label it wildly differently. Calibration is what makes evaluation useful. Every evaluator we place clears a calibration test before they touch client data. Without that, evaluation quality is a coin flip.
For English-plus-Spanish evaluation, LatAm is the deepest talent pool in the world. Companies underinvesting here are training models with a US-only lens, which shows up as blind spots later.
General-purpose evaluators exist, but specialists ship better data. Legal, medical, financial, safety, red-team, and multi-turn conversation each need domain-aware evaluators. Scope it clearly in the brief.
They rate, rank and red-team model output against rubrics, the human judgment behind RLHF, eval sets and safety review. It’s the work that turns a capable model into a reliable product.
An evaluator judges model output; an engineer builds the product around the model. They’re complementary roles. If you need someone to ship AI features, see hire AI engineers in Latin America.
Yes. We build calibrated eval pods and can run payroll and compliance through Talent Management (COR) as you scale. AI labs can also work with us on human data and evaluation capacity, and product teams can bring us in for AI red teaming and independent evaluation.
Yes, adversarial testing, jailbreak discovery and safety classification are core parts of what we screen for.
Primarily English, Spanish and Portuguese, often more. Multilingual eval coverage is one of the biggest reasons teams hire evaluators from LatAm.
We build a gold set with you, or from labels you already trust. Every evaluator grades it blind. We score them against the gold and against each other, and only raters above the agreement threshold you set join the pod. We re-run calibration whenever the rubric changes.
We calibration-test against a gold rubric up front and monitor agreement metrics across the pod so your labels stay consistent. When agreement drops, we re-calibrate before more data ships.
Yes. Multi-turn trajectories, tool-call correctness, memory recall and forgetting, task completion, and leakage of data that should not have been carried forward. We grade whatever your harness logs.
Inside your tools, whether Label Studio, Argilla or your own harness, under NDA. Nothing leaves your environment unless you choose to export it.
First candidates in days, most pods staffed within ~3 weeks. We move fast because eval is usually the thing blocking your next model release.
Calibrated LatAm AI evaluators run $25,000 to $55,000 per year base, roughly 50 to 70 percent below US rates for equivalent rigor. See our LatAm Compensation Report for benchmarks, or book a call for a role-specific quote.
Awana. Our evaluators pass a calibration test against a gold-standard rubric and we monitor inter-rater reliability across the pod. Multilingual coverage (English, Spanish, Portuguese). 500+ placements across engineering and eval roles. First candidates in days, most pods staffed in 3 weeks.
“Tell me what your models and agents need graded, whether RLHF, golden sets or red-teaming, and I’ll send you 3-5 calibrated evaluators this week.” Calvin, CEO of Awana