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AI powered digital newsgathering

Senior Linguistic Engineer

£45k – £65k • 0.0% – 1.0%
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This is a highly specialised and challenging role with opportunity for significant personal development. The skills required to excel in it are rare, and demand for them is growing rapidly.

Krzana finds news for journalists.

We run a Natural Language Understanding pipeline that processes most of the real-time news and social media content on the web.

We currently process text in English, German, French and Spanish.

We're looking for a senior engineer to improve our system's understanding of the languages we already cover, and extend it to new ones. The initial languages will include Hindi, Dutch and Greek, and there will be scope to extend coverage further.

We have a strong in-house training culture and you will receive significant support, should that be required.

You will have a strong linguistic background and aptitude for human language, ideally speaking several of the system's current or future languages fluently.

All of our developers are expected to have an exceptional aptitude for problem solving and a deep interest in programming and their own personal improvement.

Senior developers will have significant commercial software development experience, which will be underpinned by a strong understanding of, and instinct for, fundamental software principles.

Experience with our stack is a nice kickstart, but by no means required. We write in Python and optimise in Cython. We work with wordnet, and a variety of flavours of word, sentence and lemma vectors. You'll be working with excellent core systems and natural language understanding engineers and researchers.

This is an area of active research and development. You will be expected to learn new tools and frameworks on the job.

You will be expected to implement existing algorithms across languages, and come up with novel approaches to extracting meaning from text in languages with highly varying linguistic structure.


The backbone of our company is our text processing pipeline, which is at the leading edge of NLP/NLU. We identify and disambiguate between tens of millions of entities and hundreds of thousands of events and concepts in both well- and poorly-written unstructured text, in multiple human languages, in real time.

Applied NLU is a combination of linguistics, machine learning, AI, implementing (and optimising) other people's research, doing original research, and pure software engineering. Some of the algorithms in use were conceived in the '70s, others were first dreamt up this year, some have beautiful commercial wrappers around them (like SpaCy) and others have never seen proper industrial-grade implementations before.

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