NLP engineer job description.

Looking for an NLP engineer or preparing for a move into language-focused AI? This NLP engineer job description outlines model development, data pre-processing, and toolsets like spaCy and Hugging Face — along with career progression options and current market pay rates.

Table of contents

    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

    MEET THE TEAM

    Meet our team of data recruiters.

    Harry Griffiths
    Harry Griffiths

    Co-Founder

    Luke Rose
    Luke Rose

    Development - Europe

    Zak Jones
    Zak Jones

    DevOps, Cloud & Infrastructure - UK

    salary guide

    Our UK data salary guide.

    NLP engineers develop models that interpret and generate human language. Salary should reflect experience with tokenisation, transformers, and text processing at scale.

     

    Use our UK data salary guide to benchmark NLP roles, explore 2024 hiring patterns, and prepare for changes through to 2026.

    FAQS

    Natural language processing (NLP) engineer FAQs.

    Yes — but the role has changed. While many companies use OpenAI or Claude APIs, NLP engineers are needed to fine-tune models, manage prompt engineering at scale, build evaluation frameworks, and enforce safety constraints. There’s also demand for those who can embed NLP into production systems.

    Legal tech, healthcare, customer support automation, and fintech. These sectors often deal with large volumes of text, contracts, or user-generated data — and need NLP to structure, classify, or summarise it intelligently and securely.

    Candidates who understand text pre-processing, data leakage risks, evaluation metrics (e.g. BLEU, ROUGE, F1), and how to balance model performance with interpretability. Familiarity with Hugging Face and experience working with domain-specific corpora is a good signal.

    Rarely from scratch — most work involves fine-tuning or distilling existing models. The value now lies in selecting, adapting, and safely integrating models — not building foundational ones from zero.

    Progression could lead to applied AI lead, prompt optimisation manager, or ML product roles. Some also transition into LLMOps or responsible AI governance roles as systems mature.

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