Get NLP Engineers
Get NLP Engineers from Latin America
Natural language processing engineers from Brazil, Argentina, and Colombia who build text classification systems, information extraction pipelines, semantic search, and LLM-powered applications. They work in transformer models, not just API wrappers.
Why LATAM for NLP engineering
Cost advantage on specialized talent
Senior NLP engineers in the US earn $180k-$230k. LATAM equivalents deliver at $80k-$120k, meaningful savings for applied NLP roles that require ongoing model development.
Timezone overlap
LATAM's 4-8 hour overlap with US teams supports collaborative model evaluation sessions, which often require real-time annotation review and prompt design iteration.
Multilingual NLP capability
LATAM NLP engineers work in Portuguese, Spanish, and English simultaneously. This cross-lingual experience is relevant for multilingual text applications and for understanding tokenization and linguistic edge cases.
Academic NLP backgrounds
Universities in Brazil and Argentina have active NLP and computational linguistics research groups. Several engineers in our network have graduate NLP backgrounds.
Applied LLM engineering experience
LATAM NLP engineers are increasingly building production LLM applications: RAG pipelines, evaluation frameworks, fine-tuning workflows, and context management systems.
What our NLP engineers know
Evaluation frameworks (RAGAS, TruLens)
FastAPI for NLP servingHow we vet NLP engineers
Every candidate completes all seven stages before you see their profile. You can also run your own technical round after our screening.
* For common roles like full-stack. Other roles typically take 7 to 10 days.
CV check
Stage 1We review experience, stack, seniority, and career history against the role before anyone is invited to interview. Most applicants stop here.
Behavioral interview
Stage 2A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
Technical interview 1
Role-specificA 90-minute live session covering three areas. First, classical NLP: given a text dataset, candidates design a classification pipeline: preprocessing, feature engineering, model selection, and evaluation metrics (precision, recall, F1 with class imbalance). Second, transformer models: fine-tuning a BERT-based model for a given task, handling tokenization edge cases, and managing sequence length constraints. Third, LLM application design: candidates design a RAG pipeline for a given use case: chunking strategy, embedding model selection, retrieval evaluation, and hallucination mitigation. We also cover multilingual text handling and how they'd evaluate model performance across languages.
Technical interview 2
Stage 4A second, deeper round on system design, architecture tradeoffs, and code review, matched to the seniority you are hiring for.
Take-home assessment
Stage 5A practical assignment that mirrors real work. We review code quality, testing, documentation, and how the candidate explains their decisions.
Reference check
Stage 6We speak with at least two professional references who worked with the candidate, not personal contacts.
Background check
Stage 7Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Who you might hire
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
Mid NLP Engineer
São Paulo, Brazil
4+ years experience
Senior NLP Engineer
Buenos Aires, Argentina
7+ years experience
Lead NLP Engineer
Florianópolis, Brazil
10+ years experience
Want to see profiles matched to your specific requirements?
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What to expect
What sets our NLP engineers apart
NLP engineers in our network know that deploying a language model and deploying a reliable language application are different problems. They build evaluation frameworks before they deploy, think about failure modes in multilingual text, and use AI tools to accelerate both their research and their implementation work.
LLM-assisted paper-to-code translation: they use Copilot to help implement research papers faster when incorporating new architectural ideas into production pipelines.
They build evaluation frameworks before they ship. RAGAS and TruLens are used to measure RAG pipeline quality, not just hope it works.
Context window management, chunking strategies, and metadata filtering are designed upfront, not retrofitted after retrieval quality issues appear.
They document model cards for every model they deploy: training data, evaluation benchmarks, known failure modes, and intended use.
Cross-lingual edge cases (tokenization issues in Portuguese, script normalization in non-Latin text) are tested and documented before models go to production.
LATAM vs US NLP engineer salaries
Junior NLP Engineer
Mid NLP Engineer
Senior NLP Engineer
Lead NLP Engineer
FAQ
Common questions
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