AYLIEN is hiring an NLP Engineer to join the Text and Image Analysis startup in Dublin, Ireland.
This is a unique opportunity to work with a team of talented Scientists and Engineers at AYLIEN to push the boundaries of NLP.
AYLIEN is a leading Text and Image Analysis solution provider in Europe, helping thousands of developers and data scientists in more than 500 cities globally to extract meaning and insights from unstructured data.
For more information please visit aylien.com.
- Improve and extend NLP capabilities of AYLIEN’s Text Analysis engine.
- Research and evaluate new/different approaches to NLP problems.
- Produce deliverable results and take them from development to production in collaboration with our engineers.
- Engage in knowledge sharing with rest of the team.
You must have:
- Strong Machine Learning background and familiarity with R, WEKA, RapidMiner, etc.
- Expertise in at least 3 of the following: Sentiment Analysis, Entity Extraction, Document Classification, Topic Modeling, Natural Language Understanding (NLU) and Natural Language Generation (NLG).
- Strong understanding of text pre-processing and normalization techniques, such as tokenization, POS tagging and parsing and how they work at a low level.
- Strong knowledge of Java or Python, and general software development skills (source code management, debugging, testing, deployment, etc.)
- Expertise in producing, processing, evaluating and utilizing training data.
Would be great if you have:
- MSc./PhD in Computer Science, Computational Linguistics or related fields.
- Good understanding of linguistics and language as a phenomenon.
Strong interest in, and knowledge of Artificial Intelligence and its subfields.
- Experience with non-English NLP.
- Experience with Deep Learning and Word Embeddings.
- Experience with open-source NLP toolkits such as CoreNLP, OpenNLP, NLTK, gensim, LingPipe, Mallet, etc.
- Experience with open-source ML/math toolkits such as scikit-learn, MLlib, Theano, NumPy, etc.
- Experience with noisy and/or unstructured textual data (e.g. tweets)
Published work in academic conferences/journals or industry circles.