Hiring nowLanguage and multilingual intelligence

Natural Language Processing Scientist

Also called NLP Scientist · Computational Linguist

An NLP Scientist builds and tests methods for language understanding and generation. They make sure a company's language models work across languages and user groups, and they show where and why the models fail.

What they do

They develop or adapt transformer-based models for tasks such as classification, extraction, translation or generation. They study how text is represented, including how tokenisers split different languages, and they test whether those choices cause problems. They build evaluation sets in several languages, run experiments, and read a lot of model output by hand. They sort errors into a taxonomy: what went wrong, how often, and for whom. They then change data, training or modelling to fix the biggest gaps, and they explain the results to engineers and product teams in plain terms.

What a strong hire delivers

In the first months, a strong hire delivers a working language model or task model and a clear error taxonomy that the team can act on. They set up multilingual evaluation that reports quality by language and by user group, not just one average. They find the two or three failure patterns that matter most, fix at least some of them, and leave the team with evaluation it trusts.

How you can tell they are good

Ask them to walk through a model they built and break down its errors. A strong candidate sorts failures by linguistic cause, not just by score. Ask how they would test a model in a language they do not speak. Listen for use of native-speaker review, careful sampling and caution about translated test sets. Be wary of anyone who quotes a single headline metric.

Why it is hard to fill

The title is used loosely. Some NLP Scientists are really machine learning engineers, others are linguists who rarely train models. The strong ones do both: they can run transformer experiments and explain why a language behaves as it does. That mix is scarce. Many candidates come from academic NLP or linguistics, so titles vary and CVs can be hard to read. We screen for evidence of real error analysis.

Where to find them

Look in NLP and computational linguistics research groups, language technology teams, and translation and localisation technology companies. Titles include Computational Linguist, Research Scientist, Applied Scientist and Machine Learning Engineer (language). Some come from speech and search teams, or from linguistics degrees followed by hands-on modelling work. Those with multilingual experience often come from companies serving many markets.

Indicative pay

Not enough published evidence yet to give a fair range for this role, which is common for work this new. We benchmark it for you as part of a search.

Interview questions for a Natural Language Processing Scientist

  1. Tell me about a language model you built or adapted. What did the error analysis show?

    A strong answer names specific error categories, how they were found and what changed as a result. It should include examples. Worry if they only cite aggregate scores or cannot describe a single concrete failure.

  2. How would you evaluate a model in a language nobody on the team speaks?

    Look for native-speaker reviewers, carefully built test sets, and caution about machine-translated benchmarks. They should say what they would not trust. Worry if they assume English results carry over.

4 more questions, with what a strong answer covers, are in the Role Kit.

Job description you can post

We are building language products that need to work well for people in many languages and communities. We are looking for an NLP Scientist to develop language understanding and generation methods, and to show clearly where our models succeed and fail.

What you will do

  • Develop and adapt transformer models for language understanding and generation tasks
  • Design text representation choices, including tokenisation, for multiple languages
  • Build multilingual evaluation sets and report quality by language and demographic slice
  • Produce an error taxonomy for each model and prioritise fixes
  • Run experiments and turn findings into changes in data, training or modelling
  • Work with engineers and product teams to ship improvements
  • Write up results clearly for non-specialists

What you will bring

  • Demonstrated work building or adapting transformer models for language tasks
  • Examples of detailed error analysis, not only headline metrics
  • Experience with tokenisers and text representation across more than one language
  • Ability to design and critique evaluation methods
  • Solid linguistic understanding, shown through past projects
  • Clear written and spoken communication

Nice to have

  • Experience with low-resource languages
  • Background in translation or localisation
  • Published or open work in NLP or computational linguistics

Free to use and adapt. If it saves you time, a link back to this page is appreciated.

Skills that matter

  • Transformer modellingAdvancedImplement attention-based sequence or multimodal models
  • Representation learningAdvancedLearn transferable features or embeddings
  • Linguistics and localisationWorkingAdapt language behaviour across cultural and linguistic contexts
  • Model evaluationWorkingMeasure capability and failure modes credibly

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How we wrote this

Role definitions are our own editorial work, checked against occupational classifications, standards, research and employer pages. Titles vary by company, and this is a description of the work, not a job advert.

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