Company description

We are creating a cutting-edge product above modern technologies
making it possible to audit and verify content against bad signals and falsification.

More on https://buster.ai

Job description

NLP Deep Learning Researcher

Responsabilities
As a Deep Learning Engineer/Researcher, you will be part of a team that builds a cutting-edge product on top of modern technologies (We know you love to read this stuff).

You will develop NLP/NLU Deep Learning new models with other members of our team. You will be 100% dedicated to research in the field of machine reading, learning and comprehension. This includes - but not limited - to build a robust semantic text graph representation, to create new ways to walk through this graph for fast information retrieval and to greatly improve state of the art on SMATCH (Semantic Matching). By improving we really mean setting up a new standard in this field!

Our team work closely with highly reputated researchers. 

Perks
Good salaries (with or without stock-options: to be discussed)
Possibility (encouraged) to publish research papers
Offices with free coffee (if that’s your poison), meeting rooms, white boards
You are admin of your laptop \o/ and have an additional screen
Cloud based infrastructure and state of the art technology
We use open source software and are ready to contribute
Cool young team with team-building, meet-up and afterwork
Work from home possibilities (to be discussed)

Desired profile

Minimum qualifications
PhD degree in Computer Science, Applied Mathematics, Machine Learning or related field
Experience in NLP/NLU algorithm development
Experience in a strongly typed language including but not limited to: C++, Rust, Scala, Java  
Experience in a scripting language including but not limited to: Python or JavaScript 

Preferred qualifications
Experience with research development of recent models like BERT, GPT-2, XLNET, LSTM using either PyTorch or TensorFlow
Great autonomy and summarizing capabilities of the recent publications and research papers (Arxiv Sanity is your best friend).
Graph learning, graph mining, graph pooling for text representation and summarization
Ultra-stemming and Lemmitization for summary generation
Experience in clustering, topic modelling, recommendation systems, targeting systems, ranking systems
Good coding practices: Git, Testing, Documentation, CI/CD