Your LATAM Hiring Department.
LATAM teams for RLHF, SFT and physical AI training data — teleoperation, egocentric video and robotics annotation. Cohen's Kappa ≥ 0.80 QA, US time zones, from $18/hr. Scale from a 10-person pilot to 400+ specialists with zero data leakage.
Recruit, vet, and onboard annotators in ≤72 hours.
4.7× faster output vs. generic crowdsourcing platforms.
Cohen's Kappa ≥ 0.80 with contract-level guarantees.
−48% average handle time with dual review and QA gates.
0.0% data leakage on golden sets with SOC-style controls.
We are your strategic partner in Latin America, building high-performance teams that scale with your vision
We commit to measurable agreement targets (Cohen's Kappa ≥ 0.80) with clawbacks if we fall short. Your data quality is our responsibility, backed by SLAs.
Payroll, compliance, PTO, training, and free replacements—all included. You focus on model architecture; we handle the human workforce.
Works with Hugging Face, PyTorch, Label Studio, Scale AI, or your custom annotation tools. We adapt to your ML pipeline, not the other way around.
US time-zone overlap, English-fluent, tri-lingual squads (EN/ES/PT) at competitive rates. Cultural alignment + technical depth.
Ready to build your LATAM advantage?
Comprehensive solutions designed specifically for your industry needs
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Advanced tools for seamless collaboration
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Preference ranking, pairwise comparison, reward model data generation
Stack:
Instruction-response pairs, taxonomy tagging, rubric execution
Stack:
SWE-bench evals, UI intents, function calling, toolformer traces
Stack:
Audio/vision labeling, speech analysis, safety red-teaming
Stack:
We source bilingual annotators and subject-matter experts (EN/ES/PT) from our vetted LatAm network and screen them within 72 hours.
Build custom annotation rubrics, golden test sets, leakage controls, and calibration workflows tailored to your LLM task.
Ramp from pilot (10 annotators) to production (400+) with US time-zone coverage, sprint cadences, and agile iteration.
Maintain Cohen's Kappa ≥ 0.80, dual review on critical tasks, spot audits, and SOC-style security controls with zero data leakage.
Weekly QA reports, A/B test rubric changes, refresh golden sets, and optimize cost per labeled unit while maintaining quality targets.
Real results from companies using AI-powered hiring
"Hireslink's AI matching increased our team efficiency by 60%. The personality filtering helped us find developers who truly fit our culture."
Sarah Chen
CTO, TechFlow
"We hired 5 developers in 2 weeks instead of 3 months. The AI predictions were spot-on for both technical skills and work style."
Marcus Rodriguez
VP Engineering, DataCorp
"The quality of Latin American talent is exceptional. AI matching helped us build a diverse, high-performing remote team."
Jennifer Kim
Head of Product, InnovateLabs
"98% match accuracy isn't just a number - it's the difference between hiring success and costly mistakes. Hireslink delivers."
David Thompson
CEO, ScaleUp
Join 200+ companies saving 55% on talent costs. Get matched with perfect candidates in ≤72h.
Three ways to work with us
Same network, same vetting bar. The difference is who employs the person and who runs the HR layer.
For companies building a team.
HiresLink manages the hiring and HR/operational layer month to month.
For companies making one specific key hire.
You employ the person directly. One-time fee, no recurring management fee.
For companies adding capacity to an existing team.
Vetted LATAM specialists plug into the team and processes you already run.
Real KPIs from our human-data operations—measured, reported, and guaranteed
Ready to achieve these metrics?
See how HiresLink stacks up against other RLHF/SFT staffing options for enterprise LLM training
| Feature | HiresLink | Generic Crowdsourcing | Traditional Staffing |
|---|---|---|---|
| Specialized in LLM Human Data (RLHF/SFT/Annotation) | |||
| Contract-Level QA Guarantees (Cohen's Kappa ≥ 0.80) | |||
| Zero Data Leakage on Golden Sets | |||
| Rapid Ramp (10→400+ annotators in weeks) | |||
| Full HR + Payroll + Compliance | |||
| Multimodal Expertise (Audio/Vision/Code) | |||
| US Time-Zone Coverage | |||
| Infra-Agnostic (HuggingFace, Label Studio, Custom) |
Comparison based on typical market offerings. Features may vary by provider.
See the difference for yourself
Learn how to design production-grade human-data operations for enterprise AI
Reinforcement Learning from Human Feedback (RLHF) and Supervised Fine-Tuning (SFT) are complementary approaches to improving LLMs with human-generated data. Understanding when to deploy each can dramatically impact model performance, safety, and alignment.
Key Insight
Use SFT when you need the model to follow specific instructions or formats. Deploy RLHF when you want to learn nuanced preferences, avoid harmful outputs, or optimize for subjective quality metrics.
SFT trains your LLM on high-quality input-output pairs. You provide examples of desired behavior: a prompt and the "correct" response. This works exceptionally well when:
RLHF trains a reward model that captures human preferences. Annotators rank or rate multiple model outputs, teaching the system what "good" looks like in subjective scenarios. RLHF excels when:
At HiresLink, we staff teams for both: SFT annotators who create clean instruction-response pairs, and RLHF specialists who perform pairwise comparisons. Our QA ensures high agreement (κ ≥ 0.80) whether you're running SFT, RLHF, or both in parallel. Learn more about our AI specialists and how we help SaaS companies and AI startups scale their annotation operations.
Quality assurance for human-generated data isn't optional—it's the foundation of reliable LLM training. Poor QA leads to noisy labels, downstream hallucinations, and wasted compute. Here's how to build production-grade QA that scales.
Cohen's Kappa (κ) quantifies agreement between annotators, accounting for random chance:
Track Kappa weekly per task type. If agreement drops, pause labeling and diagnose: rubric unclear? Need retraining? Edge cases emerging?
A golden set is curated labeled examples with known "correct" answers. Use them to:
Best Practice
Build your golden set iteratively. Start with 100-200 examples covering common scenarios and edge cases. Refresh quarterly as your model evolves.
Data leakage—when annotators memorize or copy from training/test sets—corrupts evaluation. Mitigation:
For high-stakes labels (safety, compliance, golden-set creation), implement dual review: two independent annotators label the same task. If they agree, finalize. If not, a senior reviewer adjudicates.
At HiresLink, we design QA workflows tailored to your risk profile and budget. Contract-level guarantees ensure predictable quality with transparency—weekly Kappa reports, golden-set dashboards, and leakage audits included.
Real outcomes from AI companies building production LLMs with human-generated data
Throughput Growth
1,000 tasks in 8 days (code + UI annotation)
Average Handle Time Reduction
+76% approval rate in multimodal annotation pipeline with dual review
RLHF Pods Scaled in 7 Days
0.0% leakage on golden sets, κ = 0.83 maintained throughout ramp
Join leading AI companies building production LLMs with measurable quality guarantees
AI training data services
Ten AI data services staffed from Latin America, each with defined deliverables, a quality metric written into the contract, and a bilingual team working your hours. Pick one service or run several in the same pod.
Pairwise comparisons, rankings and critique loops that turn human judgement into reward-model training data for PPO and DPO pipelines.
Deliverables
Instruction-response pair creation, taxonomy tagging and rubric execution for domain-specific fine-tuning in English, Spanish and Portuguese.
Deliverables
Net-new human-generated data: prompts, long-form writing, speech recordings, screen and device capture, and scenario scripting for edge cases your logs never contain.
Deliverables
Adversarial testing of deployed and pre-release models: jailbreaks, prompt injection, harmful content probing and agentic misuse scenarios.
Deliverables
Human evals, LLM-as-judge calibration, golden datasets and regression suites so every prompt or model change is measured instead of guessed.
Deliverables
Native Spanish, Portuguese and English data creation, translation review, and locale-aware safety labeling for models serving the Americas.
Deliverables
SWE-bench style task annotation, tool-calling traces, UI intents and agent trajectories labeled by engineers who read and run the code.
Deliverables
Vision, audio and video labeling: bounding boxes, segmentation, transcription, diarization and cross-modal alignment checks.
Deliverables
Task environments, simulators and verifiable reward functions for training and evaluating agents on real software workflows.
Deliverables
Managed teleoperation sessions, manipulation trajectories and force/torque traces for training and evaluating real-world robot policies.
Deliverables
First-person capture with depth, segmentation, hand pose and action boundaries — the interaction video world models and VLA systems cannot scrape.
Deliverables
Licensed and credentialed professionals — clinicians, lawyers, accountants, engineers — writing and reviewing data where a generalist annotator cannot.
Deliverables
Which profile executes each data type, what it is typically used for, and what it costs per hour with a dedicated LATAM team.
| Modality | Typical use cases | Who executes it | LATAM rate |
|---|---|---|---|
| Text | Instruction tuning, preference data, summarization, safety labels | SFT specialists, RLHF annotators | $18–33/hr |
| Code | SWE-bench tasks, code review labels, unit-test generation | Software engineers annotating part-time or full-time | $30–45/hr |
| Agent traces | Tool calling, browser and terminal trajectories, failure taxonomies | LLM engineers, RL environment engineers | $32–48/hr |
| Audio & speech | Transcription, diarization, accent coverage, TTS reference recordings | Bilingual transcriptionists, linguists | $16–26/hr |
| Image | Bounding boxes, segmentation, OCR ground truth, caption quality | Multimodal annotators, CV-adjacent reviewers | $16–28/hr |
| Video | Action recognition, temporal segmentation, safety review | Multimodal annotators with QA training | $18–30/hr |
| Tabular & documents | Invoice/contract extraction ground truth, structured-output evals | Domain experts (finance, legal, health) | $22–70/hr |
Most teams start with a managed project to prove quality, then convert to a dedicated pod once the taxonomy stabilizes.
Ongoing programs with evolving guidelines and confidential data
A named team of annotators plus a QA lead and a project manager, working your hours, in your tooling, reporting weekly agreement metrics.
Pricing: Hourly per seat, monthly invoicing
Fixed-scope datasets with a defined volume and deadline
We scope the taxonomy, build the golden set, staff the team and deliver the dataset against an agreed acceptance criteria.
Pricing: Per project or per accepted unit
Teams that already run their own annotation platform and QA
We source, vet and place individual specialists who report directly to your leads. You manage the workflow; we handle contracts and payroll.
Pricing: Hourly per specialist
How we handle your prompts, model outputs and collected data across every engagement.
Every contributor signs an NDA and is named on the project. No anonymous crowd, no subcontracting without your approval.
Prompts, outputs and datasets live in your environment or a controlled cloud workspace. Nothing is stored on personal devices.
Access is limited to the task types each contributor needs, and rotated as programs change to reduce exposure.
Every label carries a timestamp, contributor ID and session metadata so you can reconstruct how any datapoint was produced.
PII-bearing tasks are redacted or routed to a restricted pod with explicit handling rules agreed before work starts.
Work-for-hire terms assign all created data to you, with consent and provenance records for collected audio, image and video.
Requirements vary by industry. If you need specific contractual controls for GDPR, CCPA or client-imposed security terms, we align them before the pod starts — see how our staff augmentation contracts work.
Language models learned from a corpus that already existed. Physical AI — robots, world models and embodied agents — has no equivalent. There is no internet-scale archive of a hand rotating a wrench, a gripper failing to seat a connector, or a mobile base negotiating a cluttered warehouse aisle with a person walking into frame. That data has to be captured, on purpose, by people.
This is why physical AI programs stall on data rather than on architecture. Teams have the policy code and the simulator; what they lack is a steady supply of real egocentric video, teleoperated demonstrations, depth and force traces, and the human judgment to label what actually happened in each episode. We staff that supply from Latin America, in your time zone, under the same QA discipline we run for RLHF and supervised fine-tuning.
The work spans four layers — perception, world modeling, policy learning and language grounding — plus the capture pipeline and compliance controls that make video-based datasets usable in production. Closely related roles live on our RL environment engineers and computer vision engineers pages.
Each layer of an embodied system consumes a different kind of human data, executed by a different profile.
What is in the scene, and where is it?
Egocentric and third-person video with depth, semantic and instance segmentation, 3D bounding boxes and object state labels.
Executed by: Multimodal annotators with CV review training
What happens next if the robot acts?
Long-horizon video of real physical interactions — contact, deformation, slippage, tool use — with optical flow and action labels.
Executed by: Video annotators + QA leads
Which action should the robot take right now?
Manipulation trajectories, teleoperation demonstrations and paired observation-action sequences with reward or success signals.
Executed by: Teleoperators and RL environment engineers
How does an instruction map to a physical action?
Video-caption pairs, natural-language instructions followed by demonstration, and step-level task decompositions.
Executed by: Bilingual annotators + domain reviewers
IsaacSim, MuJoCo and PyBullet are indispensable for volume and safety — and insufficient on their own.
Rendered scenes in IsaacSim, MuJoCo or PyBullet miss the sensor noise, motion blur, lighting variance and lens artifacts a real camera produces, so perception stacks trained purely in sim degrade the moment they see a real frame.
Friction, compliance, deformation and slippage are simplified in every simulator. Contact-rich tasks — insertion, folding, pouring, cable routing — are exactly where policies trained only in simulation break.
Cluttered counters, half-open drawers, unexpected humans in frame, objects that are wet, bent or mislabeled. Nobody writes those scenarios into a simulator; they get captured.
Simulation buys volume and safety for the base policy. Real human-captured demonstrations and annotated interaction video are what close the sim2real gap. Our teams supply the real half of that pipeline.
From an empty dataset to shards your training job can read, with a human in the loop at every stage.
Net-new data recorded to your spec, because logs from a model that has never touched the physical world do not exist.
Raw footage becomes trainable signal through derived channels and human verification of every automated output.
Judgment-heavy labeling where automation still fails, run against a golden set and a documented rubric.
Datasets shipped in the format your training stack already reads, with integrity checks and documentation.
Rates are per specialist hour from Latin America, fully managed, in US time zones.
| Modality | Use cases | Profile | Starting at |
|---|---|---|---|
| RGB video (third-person) | Scene understanding, action recognition, benchmark evaluation | Multimodal annotators | $18–28/hr |
| Egocentric video (wearable) | Hand-object interaction, first-person world models, VLA grounding | Capture collaborators + annotators | $20–30/hr |
| Depth / RGB-D | 3D boxes, grasp planning ground truth, occlusion reasoning | CV-adjacent reviewers | $22–32/hr |
| LiDAR & point clouds | Navigation, mapping, outdoor and warehouse robotics | Senior 3D annotators | $26–38/hr |
| IMU & proprioception | Motion segmentation, gait and manipulation state estimation | Robotics-literate annotators | $24–34/hr |
| Force / torque | Contact-rich manipulation, insertion and assembly policies | Teleoperators with robotics background | $28–42/hr |
| Ambient audio | Event detection, failure cues, multimodal alignment | Bilingual audio annotators | $16–26/hr |
Video and sensor capture carries obligations that text annotation never does.
Every recorded participant signs a release covering the recording, its intended use in model training and downstream redistribution rights. Records are delivered with the dataset.
Bystander faces, license plates, screens and documents can be blurred at delivery, with a human pass verifying that redaction actually landed on every frame.
Video carries PII that text pipelines never see — homes, addresses, badges, children. We scope capture locations up front and review batches against an agreed exclusion list.
Data stays in your buckets or an approved environment, with role-based access, audit logs, no local persistence and an agreed retention and deletion window.
Every AI training data service we staff from Latin America — RLHF, supervised fine-tuning, data collection, red teaming, evaluation, multilingual data, agentic traces and domain expert networks — plus the roles and salary benchmarks behind them.
Pairwise comparisons, rankings and critique loops that turn human judgement into reward-model training data for PPO and DPO pipelines.
$26–36/hr · Inter-annotator agreement κ ≥ 0.80Instruction-response pair creation, taxonomy tagging and rubric execution for domain-specific fine-tuning in English, Spanish and Portuguese.
$23–33/hr · Label accuracy ≥ 95% on golden setsNet-new human-generated data: prompts, long-form writing, speech recordings, screen and device capture, and scenario scripting for edge cases your logs never contain.
$18–28/hr · Spec compliance ≥ 97% at acceptance samplingAdversarial testing of deployed and pre-release models: jailbreaks, prompt injection, harmful content probing and agentic misuse scenarios.
$28–40/hr · Attack coverage across 12+ harm categoriesHuman evals, LLM-as-judge calibration, golden datasets and regression suites so every prompt or model change is measured instead of guessed.
$25–35/hr · Judge-human correlation tracked per releaseNative Spanish, Portuguese and English data creation, translation review, and locale-aware safety labeling for models serving the Americas.
$20–30/hr · Native reviewer sign-off on 100% of batchesSWE-bench style task annotation, tool-calling traces, UI intents and agent trajectories labeled by engineers who read and run the code.
$30–45/hr · Functional correctness verified by test runsVision, audio and video labeling: bounding boxes, segmentation, transcription, diarization and cross-modal alignment checks.
$18–30/hr · Annotation precision audited on blind golden setsTask environments, simulators and verifiable reward functions for training and evaluating agents on real software workflows.
$32–48/hr · Deterministic reproduction on 100% of tasksManaged teleoperation sessions, manipulation trajectories and force/torque traces for training and evaluating real-world robot policies.
$28–42/hr · Deterministic replay checks on every episodeFirst-person capture with depth, segmentation, hand pose and action boundaries — the interaction video world models and VLA systems cannot scrape.
$20–30/hr · Annotation agreement κ ≥ 0.80 on action boundariesLicensed and credentialed professionals — clinicians, lawyers, accountants, engineers — writing and reviewing data where a generalist annotator cannot.
$35–70/hr · Credential verification on every expertEverything you need to know about human-data staffing for LLM training
Recruit, train, and manage vetted LatAm annotation teams with measurable QA guarantees. Get started in ≤72 hours.