Find remote NLP Engineer jobs.
Build language AI.

Create a free profile and browse the NLP engineer positions on this page. Lead your profile with your domain expertise and the specific NLP tasks you have worked on: text classification, NER, question answering, multilingual systems. Companies searching for NLP engineers often search by domain (medical, legal) or specific task, so make these explicit in your profile headline and project descriptions.

It depends on seniority, location and the company, so we do not quote a typical figure. Many listings on this page show the pay range the employer published: compare those directly, and check whether the range is base salary or includes equity.

Hugging Face Transformers, BERT family models, spaCy, your specific NLP tasks (NER, text classification, information extraction, summarization), evaluation metrics used (F1, ROUGE, BERTScore), and any dataset or annotation work. If you have multilingual experience, highlight it prominently: it is rare. Also mention any LLM-based NLP work (RAG, zero-shot classification, prompt-based extraction) if applicable.

Yes, for normal use. Creating a profile, browsing jobs and applying are free, up to 20 applications a day. Credits are optional: they pay for applications beyond that limit or to put an application at the top of the list. No placement fee when you get hired, no share of your salary, no exclusive contract.

The most future-proof NLP engineers understand both approaches (fine-tuned task-specific models and LLM-based solutions) and know when each is appropriate. LLMs are not always the right answer: for low-latency production systems, privacy-sensitive data, or very specific classification tasks, fine-tuned smaller models often outperform LLMs on cost and accuracy. Engineers who can evaluate the trade-offs and make the right architectural choice are more valuable than those who only know one approach.

NLP engineering roles typically involve training or fine-tuning language models on task-specific data, building annotation pipelines, and designing evaluation protocols for specific NLP tasks. Prompt engineering roles focus on designing prompts and retrieval systems for LLM-based products, without necessarily training models. Many NLP engineers have expanded into prompt engineering and LLM work, and profiles that can do both cover more listings.

Yes. Companies building products for non-English markets, for example Arabic, Chinese, Hindi or low-resource languages, need engineers with that experience. If you have worked with multilingual model architectures (mBERT, XLM-R), cross-lingual transfer learning, or language-specific tokenization challenges, this is a strong differentiator worth featuring prominently in your profile.

Yes, if relevant to the roles you are targeting. Academic publications in NLP signal deep technical knowledge and are valued, especially for research-leaning roles at larger companies. However, for production engineering roles, complement publications with evidence of applied work: models deployed in production, systems that real users depend on, and practical trade-offs navigated. Companies hiring production NLP engineers want to see that you can translate research into working software: publications alone are not sufficient.