NLP engineer job description.

What does a NLP engineer do?

 

An NLP engineer builds systems that allow machines to understand, interpret, and generate human language. They work on applications like chatbots, sentiment analysis, search algorithms, and voice recognition.

 

Key tasks include training and fine-tuning language models, tokenisation, intent classification, and building pipelines that transform raw text into structured data. They use libraries like spaCy, Hugging Face Transformers, and tools such as Python, PyTorch, or TensorFlow.

 

In smaller teams, they may manage the full NLP lifecycle from data preparation to production deployment. In larger organisations, they focus on specific language tasks or models embedded into customer-facing products.

 

Key responsibilities of a NLP engineer.

 

NLP engineers build systems that process and understand human language. Common responsibilities include:

• Training models for entity recognition, sentiment analysis, or summarisation

• Tokenising and vectorising large text corpora

• Fine-tuning transformer models like BERT, GPT, or RoBERTa

• Creating NLP pipelines for chatbots, search engines, or document classification

• Evaluating models using BLEU, ROUGE, or F1 scores

• Collaborating with product teams on feature development

• Managing data pipelines and annotation workflows

• Writing scripts for parsing, cleaning, and transforming raw text

• Deploying NLP models via APIs or real-time services

• Documenting methodologies and managing model drift

This role blends applied machine learning with linguistics and data handling.

 

Skills and requirements for a NLP engineer.

 

NLP engineers develop systems for understanding human language. Employers typically look for:

• 3–6 years of experience in NLP or machine learning

• Strong Python skills and familiarity with NLP libraries (spaCy, Hugging Face)

• Experience with text classification, summarisation, or chatbots

• Skilled in tokenisation, vectorisation, and language modelling

• Familiarity with transformers and LLMs

• Understanding of performance metrics and error analysis

• Ability to handle unstructured or multilingual data

• Confidence deploying models to production

• Comfortable presenting model behaviour and insights

Most NLP engineers build real-world search, recommendation, or support systems.

 

Average salary for a NLP engineer.

 

In the UK, the average salary for an NLP engineer typically ranges from £53,000 to £80,000, based on language model training, data pipeline creation, and domain knowledge.

• Mid-level NLP engineers typically earn between £53,000 and £66,000

• Senior engineers fine-tuning large language models or creating production-ready APIs may earn between £67,000 and £80,000

• Specialised knowledge of transformers, sentiment analysis, and dialogue systems boosts salary

High-paying employers include generative AI companies, enterprise search engines, and virtual assistant platforms.

 

Career progression for a NLP engineer.

 

An NLP engineer focuses on building systems that understand and generate human language. This role combines machine learning with linguistic understanding and typically progresses into advanced AI or leadership. A common path includes:

 

Junior ML engineer / NLP researcher

 

Supports dataset curation, preprocessing, and assists with training language models.

 

NLP engineer

 

Develops models for entity extraction, sentiment analysis, summarisation, and conversational AI.

 

Senior NLP engineer

 

Leads NLP projects. Works on LLM fine-tuning, embeddings, and domain-specific language models.

 

NLP architect

 

Designs scalable NLP pipelines and aligns model performance with business applications.

 

Head of AI

 

Leads strategic NLP initiatives across product lines. Advises on ethical AI use and product integration