Post an NLP Engineer job.
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Click "Post a job — it's free" — no credit card or subscription required. Define the language AI problem specifically: text classification, named entity recognition, document understanding, multilingual search, or a RAG chatbot are all very different roles. Specify the language requirements (English-only or multilingual), the domain if relevant (medical, legal, financial), and the expected input data format. List required tools and frameworks (spaCy, Hugging Face Transformers, LangChain) and set a compensation range. This level of specificity dramatically improves application quality. Most NLP roles receive their first applications within 24–48 hours of posting.

The talent section on this page is pre-filtered for NLP and prompt engineering specialists. Browse profiles showing headline, core skills, and background. Click any profile to review their NLP projects in detail: which models they have fine-tuned, on what datasets, for which downstream tasks, and what evaluation metrics they used. Filter by experience level and availability. For NLP specifically, look for profiles that mention domain-specific work (medical NLP, legal document processing, multilingual systems) — generic NLP profiles without domain context are often weaker fits for specialized applications.

Hugging Face Transformers, BERT, RoBERTa, DistilBERT, ELECTRA, T5, spaCy, NLTK, Gensim, text classification, named entity recognition (NER), relation extraction, coreference resolution, semantic similarity, sentiment analysis, question answering, summarization, machine translation, multilingual NLP, low-resource NLP, RAG pipelines, and LLM fine-tuning with LoRA and QLoRA. Evaluation frameworks: ROUGE, BLEU, BERTScore, F1, and task-specific benchmarks. Specify which capabilities are required versus preferred — NLP is a broad field and most specialists have deep expertise in 2–3 sub-areas rather than uniform coverage across all tasks.

Yes — completely free, no conditions. Post your NLP role today without a credit card or plan selection. There are no per-post fees, no trial period limits, and no placement charges when you hire. RemoteAI is free for employers in full: job posting, browsing NLP talent profiles, messaging candidates, and managing your hiring pipeline are all included from day one. The platform is built around AI talent — companies are never charged at any stage of the process.

Most NLP job posts receive their first applications within 24–48 hours. NLP engineers with LLM and retrieval experience are highly sought after and check new postings regularly. Quality and speed both improve significantly when the job post describes a specific language problem rather than a generic NLP title. Engineers who have done medical NER or multilingual text classification before will immediately recognize a matching role and apply quickly — while a vague "NLP engineer" post attracts a wide range of candidates whose fit you have to manually assess. Specificity is the key variable.

Yes. Every profile is reviewed before being listed on RemoteAI. You see a real headline, a description of the NLP systems and models they have built, the specific tasks and datasets involved, and a work history with professional context. Strong profiles describe what problem they were solving, which architecture they chose, what evaluation protocol they used, and what the outcome was. Profiles without substantive project descriptions are not listed publicly. For NLP roles, always ask candidates during the interview to explain their evaluation approach for a task similar to yours — it is the most reliable signal of real expertise.

Yes. RemoteAI has NLP specialists with multilingual expertise covering English, French, Arabic, Spanish, Chinese (Mandarin), Hindi, German, Portuguese, Japanese, and many more languages. Multilingual NLP is a distinct specialization — it requires experience with multilingual model architectures (mBERT, XLM-R, mT5), cross-lingual transfer learning, language-specific tokenization challenges, and low-resource language handling. When posting for a multilingual NLP role, specify which languages are in scope and whether the challenge is cross-lingual generalization or language-specific fine-tuning — these require different engineering approaches and different candidate backgrounds.

Traditional NLP engineers work with task-specific models — fine-tuning BERT for text classification, training NER models on labeled datasets, building information extraction pipelines with rule-based and neural hybrid systems. LLM engineers work primarily with large foundation models (GPT-4o, Claude, Llama 3) through prompting, RAG, and fine-tuning, using them as a general-purpose layer rather than training specialized models for each task. In 2025, many NLP engineers have expanded into LLM work. If your problem involves fine-grained classification or low-resource languages, a classical NLP specialist may serve you better than an LLM generalist — the two skill sets overlap but are not identical.

Free plan: create a company account and applications stay inside RemoteAI — dashboard, messaging, AI matching, no email or external link involved. The $199 option: no account, no dashboard — candidates apply straight to your own email or link instead, and your listing is reviewed and live within one business day.