Get NLP Engineers
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.
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.
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.
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.
Brazilian and Argentine universities produce NLP researchers with publications in ACL, EMNLP, and NAACL. Several engineers in our network have graduate NLP backgrounds.
LATAM NLP engineers are increasingly building production LLM applications: RAG pipelines, evaluation frameworks, fine-tuning workflows, and context management systems.
Every candidate completes all five stages before you see their profile. You can also run your own technical round after our screening.
A timed test measuring analytical thinking, pattern recognition, and problem-solving clarity, independent of specific programming language knowledge.
A structured interview assessing communication style, conflict resolution, ownership mindset, and English proficiency in a professional context.
A 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.
Verification of work history, education, and identity with written consent, aligned with applicable privacy rules including LGPD where relevant.
We speak with at least two professional references who worked with the candidate in an engineering context, not personal contacts.
After our screening, you can optionally run your own technical round before making an offer.
Sample profiles
Anonymized profiles from our vetted talent pool. Actual candidates may vary.
São Paulo, Brazil
4+ years experience
Buenos Aires, Argentina
7+ years experience
Florianópolis, Brazil
10+ years experience
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Request matched profilesWhat to expect
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.
Junior NLP Engineer
Mid NLP Engineer
Senior NLP Engineer
Lead NLP Engineer
FAQ
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Evaluation frameworks (RAGAS, TruLens)
FastAPI for NLP serving