Post an NLP Engineer job.
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Click "Post a job". You can post one job a month for free: it stays open 14 days and is reviewed first if your company website is not verified. Paid plans cover more jobs. 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.

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, one job a month is free, with no credit card. It stays open 14 days and is reviewed first if your company website is not verified. Paid plans cover more jobs, and you can also post a single job for $99 without an account. There is no placement fee when you hire.

It depends on the role and on how specific the post is, so we do not promise a delay. Quality improves 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 recognize a matching role, 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. New profiles are 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. Many NLP engineers have now 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: you create a company account and read applications in your RemoteAI dashboard, with messaging and AI matching. One job a month, open 14 days, reviewed first if your website is not verified. The $99 option: no account and no dashboard. Candidates apply to your own email or link, and the listing is reviewed before it goes live.