NLP engineering used to mean tokenisers, CRFs, and word2vec - now it means retrieval, transformers, and LLMs sitting on top of (not replacing) the classical pipeline. Your resume must show range: you can fine-tune a BERT-style classifier when latency or cost rules out an LLM call, and you can wire up a RAG system when the task needs reasoning. Hiring managers at search teams, support-automation teams, and content platforms specifically look for both halves.
The strongest NLP resumes lead with task-level metrics (F1, accuracy, recall@k) on real labelled data - not benchmark numbers. Reference the libraries the field actually uses: Hugging Face Transformers, sentence-transformers, spaCy, NLTK for classical preprocessing, and the vector and search infra (FAISS, OpenSearch, pgvector). For senior roles, evidence of multilingual systems, low-resource techniques, and human-in-the-loop labelling pipelines is a strong differentiator.
ATS keywords: NLP, natural language processing, BERT, transformer, Hugging Face, spaCy, NER, text classification, sentence embeddings, fine-tuning, Python, PyTorch.
This example works for
NLP Engineer
Natural Language Processing Engineer
Text ML Engineer
Search Relevance Engineer
ML Engineer (NLP)
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