This free NLP Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring someone to build natural language processing systems — text classification, extraction, search, or generation. With modern LLMs, NLP work increasingly blends classic techniques with foundation models, so describe what you actually do.
NLP candidates want to know the problems (search, extraction, summarization, chat), the data, and whether the work is more classic ML or LLM-based. Be specific.
If the role is broad applied AI on top of LLMs, use the AI Engineer template; if it's pure research, use the Research Scientist template.
Writing tips
- Name the NLP problems you're solving (search, extraction, summarization, chat).
- Clarify the mix of classic NLP/ML and LLM-based approaches.
- Describe your data and how much of the role is modeling vs. engineering.
- Distinguish from broad AI engineering and pure research roles.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. Language is at the heart of [feature/area], and we're hiring an NLP Engineer to build systems that understand and generate it well.
The role
As an NLP Engineer, you'll build systems that work with natural language — extraction, classification, search, summarization, or generation. You'll combine the right techniques, from classic ML to LLMs, and ship them into production. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Build NLP systems for [your problems, e.g. search, extraction, summarization].
- Choose the right approach, from classic ML to LLMs, for each problem.
- Build datasets, train or adapt models, and evaluate them rigorously.
- Ship NLP features into production and monitor their quality.
- Partner with engineers and scientists across the AI stack.
What we're looking for
- 3+ years building NLP or ML systems.
- Strong foundations in NLP techniques and modern language models.
- Proficiency in [Python] and relevant ML libraries.
- Experience evaluating and shipping models in production.
- A pragmatic approach to choosing techniques for the problem.
Nice to have
- Experience with LLMs, embeddings, and retrieval.
- Background in [your domain or language data].
- Publications or open-source work in NLP.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- Hard language problems and the data to solve them.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does an NLP Engineer do?
- An NLP Engineer builds systems that understand and generate natural language — for tasks like text classification, extraction, search, summarization, or generation. They choose techniques ranging from classic ML to LLMs, build and evaluate models, and ship language features into production.
- Is NLP engineering still relevant with LLMs?
- Yes. LLMs have changed how much NLP work is done, but the role remains valuable: choosing when to use an LLM versus a lighter model, building evaluations, handling extraction and search at scale, and managing cost and latency. Modern NLP blends classic techniques with foundation models.
- What skills should an NLP Engineer have?
- Strong foundations in NLP techniques and modern language models, proficiency in Python and ML libraries, experience evaluating and shipping models in production, and the pragmatism to pick the right approach for each problem. Experience with LLMs, embeddings, and retrieval is increasingly important.